From d5b550d51a7a4aa86ad0a76a5c894f4f9be1a085 Mon Sep 17 00:00:00 2001 From: ahmed531998 Date: Wed, 19 Apr 2023 04:19:32 +0200 Subject: [PATCH] test --- User.py | 2 +- VRE.py | 2 +- assistedlab_content.json | 1 - assistedlab_dataset.json | 1 - assistedlab_paper.json | 1 - assistedlab_post.json | 1 - janet_content_index | Bin 221072 -> 0 bytes janet_dataset_desc_index | Bin 12511 -> 0 bytes janet_dataset_titles_index | Bin 12511 -> 0 bytes janet_paper_desc_index | Bin 31018 -> 0 bytes janet_paper_titles_index | Bin 31018 -> 0 bytes janet_post_index | Bin 61859 -> 0 bytes 12 files changed, 2 insertions(+), 6 deletions(-) delete mode 100644 assistedlab_content.json delete mode 100644 assistedlab_dataset.json delete mode 100644 assistedlab_paper.json delete mode 100644 assistedlab_post.json delete mode 100644 janet_content_index delete mode 100644 janet_dataset_desc_index delete mode 100644 janet_dataset_titles_index delete mode 100644 janet_paper_desc_index delete mode 100644 janet_paper_titles_index delete mode 100644 janet_post_index diff --git a/User.py b/User.py index 5159b2a..a31ca11 100644 --- a/User.py +++ b/User.py @@ -3,7 +3,7 @@ import os class User: - def __init__(self, username, token, num_interests=3, directory='./', interests_file='interests.json'): + def __init__(self, username, token, num_interests=3, directory='/app/', interests_file='interests.json'): self.username = username self.token = token self.num_interests = num_interests diff --git a/VRE.py b/VRE.py index 1dde594..19deb0d 100644 --- a/VRE.py +++ b/VRE.py @@ -17,7 +17,7 @@ import html2text class VRE: - def __init__(self, name, token, retriever, directory='./'): + def __init__(self, name, token, retriever, directory='/app/'): self.name = name self.token = token self.catalogue_url = 'https://api.d4science.org/catalogue/items/' diff --git a/assistedlab_content.json b/assistedlab_content.json deleted file mode 100644 index 7c98617..0000000 --- a/assistedlab_content.json +++ /dev/null @@ -1 +0,0 @@ -{"id":{"1":1,"2":2,"3":3,"4":4,"5":5,"6":6,"7":7,"8":8,"9":9,"10":10,"11":11,"12":12,"13":13,"14":14,"15":15,"16":16,"17":17,"18":18,"19":19,"20":20,"21":21,"22":22,"23":23,"24":24,"25":25,"26":26,"27":27,"28":28,"29":29,"30":30,"31":31,"32":32,"33":33,"34":34,"35":35,"36":36,"37":37,"38":38,"39":39,"40":40,"41":41,"42":42,"43":43,"44":44,"45":45,"46":46,"47":47,"48":48,"49":49,"50":50,"51":51,"52":52,"53":53,"54":54,"55":55,"56":56,"57":57,"58":58,"59":59,"60":60,"61":61,"62":62,"63":63,"64":64,"65":65,"66":66,"67":67,"68":68,"69":69,"70":70,"71":71,"72":72,"73":73,"74":74,"75":75,"76":76,"77":77,"78":78,"79":79,"80":80,"81":81,"82":82,"83":83,"84":84,"85":85,"86":86,"87":87,"88":88,"89":89,"90":90,"91":91,"92":92,"93":93,"94":94,"95":95,"96":96,"97":97,"98":98,"99":99,"100":100,"101":101,"102":102,"103":103,"104":104,"105":105,"106":106,"107":107,"108":108,"109":109,"110":110,"111":111,"112":112,"113":113,"114":114,"115":115,"116":116,"117":117,"118":118,"119":119,"120":120,"121":121,"122":122,"123":123,"124":124,"125":125,"126":126,"127":127,"128":128,"129":129,"130":130,"131":131,"132":132,"133":133,"134":134,"135":135,"136":136,"137":137,"138":138,"139":139,"140":140,"141":141,"142":142,"143":143,"144":144,"145":145,"146":146,"147":147,"148":148,"149":149,"150":150,"151":151,"152":152,"153":153,"154":154,"155":155,"156":156,"157":157,"158":158,"159":159,"160":160,"161":161,"162":162,"163":163,"164":164,"165":165,"166":166,"167":167,"168":168,"169":169,"170":170,"171":171,"172":172,"173":173,"174":174,"175":175,"176":176,"177":177,"178":178,"179":179,"180":180,"181":181,"182":182,"183":183,"184":184,"185":185,"186":186,"187":187,"188":188,"189":189,"190":190,"191":191,"192":192,"193":193,"194":194,"195":195},"paperid":{"1":1,"2":1,"3":1,"4":1,"5":1,"6":1,"7":1,"8":1,"9":1,"10":1,"11":1,"12":1,"13":1,"14":1,"15":1,"16":1,"17":1,"18":1,"19":1,"20":1,"21":1,"22":1,"23":1,"24":1,"25":1,"26":1,"27":1,"28":1,"29":1,"30":1,"31":1,"32":1,"33":1,"34":1,"35":1,"36":1,"37":1,"38":1,"39":1,"40":1,"41":1,"42":1,"43":2,"44":2,"45":2,"46":2,"47":2,"48":2,"49":2,"50":2,"51":2,"52":2,"53":2,"54":2,"55":2,"56":2,"57":2,"58":2,"59":2,"60":2,"61":2,"62":2,"63":2,"64":2,"65":2,"66":2,"67":2,"68":2,"69":2,"70":2,"71":2,"72":2,"73":2,"74":2,"75":2,"76":2,"77":2,"78":2,"79":2,"80":2,"81":2,"82":2,"83":2,"84":2,"85":2,"86":2,"87":2,"88":2,"89":2,"90":2,"91":2,"92":2,"93":2,"94":2,"95":2,"96":2,"97":3,"98":3,"99":5,"100":6,"101":6,"102":6,"103":6,"104":6,"105":6,"106":6,"107":6,"108":6,"109":6,"110":6,"111":6,"112":6,"113":6,"114":6,"115":6,"116":6,"117":6,"118":6,"119":6,"120":6,"121":6,"122":6,"123":6,"124":6,"125":6,"126":6,"127":6,"128":6,"129":6,"130":6,"131":6,"132":6,"133":6,"134":6,"135":6,"136":6,"137":6,"138":6,"139":6,"140":6,"141":6,"142":6,"143":6,"144":6,"145":6,"146":6,"147":6,"148":6,"149":6,"150":6,"151":6,"152":6,"153":6,"154":6,"155":6,"156":6,"157":6,"158":6,"159":6,"160":6,"161":6,"162":6,"163":6,"164":6,"165":6,"166":6,"167":6,"168":6,"169":6,"170":6,"171":6,"172":7,"173":-1,"174":-1,"175":-1,"176":-1,"177":-1,"178":-1,"179":-1,"180":-1,"181":-1,"182":-1,"183":-1,"184":-1,"185":-1,"186":-2,"187":-2,"188":-2,"189":-3,"190":-3,"191":-3,"192":-3,"193":-3,"194":-3,"195":-3},"content":{"1":"Recent success in scaling reinforcement learning (RL) to large problems has been driven in domains that have a well-speci\ufb01ed reward function (Mnih et al, 2015, 2016; Silver et al, 2016). Unfortunately, many tasks involve goals that are complex, poorly-de\ufb01ned, or hard to specify. Overcoming this limitation would greatly expand the possible impact of deep RL and could increase the reach of machine learning more broadly. For example, suppose that we wanted to use reinforcement learning to train a robot to clean a table or scramble an egg. It\u2019s not clear how to construct a suitable reward function, which will need to be a function of the robot\u2019s sensors. ","2":"We could try to design a simple reward function that approximately captures the intended behavior, but this will often result in behavior that optimizes our reward function without actually satisfying our preferences. This dif\ufb01culty underlies recent concerns about misalignment between our values and the objectives of our RL systems (Bostrom, 2014; Russell, 2016; Amodei et al, 2016). If we could successfully communicate our actual objectives to our agents, it would be a signi\ufb01cant step towards addressing these concerns. If we have demonstrations of the desired task, we can extract a reward function using inverse reinforcement learning (Ng and Russell, 2000). This reward function can then be used to train an agent with reinforcement learning. ","3":"More directly, we can use imitation learning to clone the demonstrated behavior. However, these approaches are not directly applicable to behaviors that are dif\ufb01cult for humans to demonstrate (such as controlling a robot with many degrees of freedom but very non-human morphology). An alternative approach is to allow a human to provide feedback on our system\u2019s current behavior and to use this feedback to de\ufb01ne the task. In principle this \ufb01ts within the paradigm of reinforcement learning, but using human feedback directly as a reward function is prohibitively expensive for RL systems that require hundreds or thousands of hours of experience. In order to practically train deep RL systems with human feedback, we need to decrease the amount of feedback required by several orders of magnitude. ","4":" Our approach is to learn a reward function from human feedback and then to optimize that reward function. This basic approach has been considered previously, but we confront the challenges involved in scaling it up to modern deep RL and demonstrate by far the most complex behaviors yet learned from human feedback. In summary, we desire a solution to sequential decision problems without a well-speci\ufb01ed reward enables us to solve tasks for which we can only recognize the desired behavior, but not is economical with user feedback. function (see Figure 1). We ask the human to score. ","5":"We found comparisons to be easier for hu- equally useful for learning human preferences. helpful. Moreover, we show that collecting feed- the learned reward function. agent maximizes predicted reward. Our experiments take place in two domains: Atari games in the Arcade Learning Environment (Belle- mare et al, 2013), and robotics tasks in the physics simulator MuJoCo (Todorov et al, 2012). ","6":"We show that a small amount of feedback from a non-expert human, ranging from \ufb01fteen minutes to \ufb01ve hours, suf\ufb01ces to learn most of the original RL tasks even when the reward function is not observable. We then consider some novel behaviors in each domain, such as performing a back\ufb02ip or driving with the \ufb02ow of traf\ufb01c. We show that our algorithm can learn these behaviors from about an hour of feedback\u2014even though it is unclear how to hand-engineer a reward function that would incentivize them. long line of work studies reinforcement learning from human ratings or rankings, including Akrour et al (2011), Pilarski et al (2011), Akrour et al (2012), Wilson et al (2012), Sugiyama et al (2012), Wirth and F\u00fcrnkranz (2013), Daniel et al (2015), El Asri et al (2016), Wang et al (2016), and Wirth et al (2016). Other lines of research considers the general problem of reinforcement learning from preferences rather than absolute reward values (F\u00fcrnkranz et al, 2012; Akrour et al, 2014), and optimizing using human preferences in settings other than reinforcement learning (Machwe and Parmee, 2006; Secretan et al, 2008; Brochu et al, 2010; S\u00f8rensen et al, 2016). ","7":" Our algorithm follows the same basic approach as Akrour et al (2012) and Akrour et al (2014). They consider continuous domains with four degrees of freedom and small discrete domains, where they can assume that the reward is linear in the expectations of hand-coded features. We instead consider physics tasks with dozens of degrees of freedom and Atari tasks with no hand-engineered features; the complexity of our environments force us to use different RL algorithms and reward models, and to cope with different algorithmic tradeoffs. One notable difference is that Akrour et al (2012) and Akrour et al (2014) elicit preferences over whole trajectories rather than short clips. So although we gather about two orders of magnitude more comparisons, our experiments require less than one order of magnitude more human time. ","8":"Other differences focus on changing our training procedure to cope with the nonlinear reward models and modern deep RL, for example using asynchronous training and ensembling. Our approach to feedback elicitation closely follows Wilson et al (2012). However, Wilson et al (2012) assumes that the reward function is the distance to some unknown \u201ctarget\u201d policy (which is itself a linear function of hand-coded features). They \ufb01t this reward function using Bayesian inference, and rather than performing RL they produce trajectories using the MAP estimate of the target policy. Their experiments involve \u201csynthetic\u201d human feedback which is drawn from their Bayesian model, while we perform experiments with feedback gathered from non-expert users. ","9":"It is not clear if the methods in Wilson et al (2012) can be extended to complex tasks or if they can work with real human feedback. MacGlashan et al (2017), Pilarski et al (2011), Knox and Stone (2009), and Knox (2012) perform experiments involving reinforcement learning from actual human feedback, although their algorithmic approach is less similar. In MacGlashan et al (2017) and Pilarski et al (2011), learning only occurs during episodes where the human trainer provides feedback. This appears to be infeasible in domains like Atari games where thousands of hours of experience are required to learn a high-quality policy, and would be prohibitively expensive even for the simplest tasks we consider. TAMER (Knox, 2012; Knox and Stone, 2013) also learn a reward function, however they consider much simpler settings where the desired policy can be learned relatively quickly. ","10":" Our work could also be seen of a speci\ufb01c instance of the cooperative inverse reinforcement learning framework (Had\ufb01eld-Menell et al, 2016). This framework considers a two-player game between human and a robot interacting with an environment with the purpose of maximizing the human\u2019s reward function. In our setting the human is only allowed to interact with this game by stating their preferences. Compared to all prior work, our key contribution is to scale human feedback up to deep reinforcement learning and to learn much more complex behaviors. This \ufb01ts into a recent trend of scaling reward learning methods to large deep learning systems, for example inverse RL (Finn et al, 2016), imitation learning (Ho and Ermon, 2016; Stadie et al, 2017), semi-supervised skill generalization (Finn et al, 2017), and bootstrapping RL from demonstrations (Silver et al, 2016; Hester et al, 2017). ","11":" We consider an agent interacting with an environment over a sequence of steps; at each time t the agent receives an observation ot \u2208 O from the environment and then sends an action at \u2208 A to the environment. In traditional reinforcement learning, the environment would also supply a reward rt \u2208 R and the agent\u2019s goal would be to maximize the discounted sum of rewards. Instead of assuming that the environment produces a reward signal, we assume that there is a human overseer who can express preferences between trajectory segments. A trajectory segment is a sequence of observations and actions, \u03c3 = ((o0, a0), (o1, a1), , (ok\u22121, ak\u22121)) \u2208 (O \u00d7 A)k Write \u03c31 (cid:31) \u03c32 to indicate that the human preferred trajectory segment \u03c31 to trajectory segment \u03c32. Informally, the goal of the agent is to produce trajectories which are preferred by the human, while making as few queries as possible to the human. ","12":" More precisely, we will evaluate our algorithms\u2019 behavior in two ways: Quantitative: We say that preferences (cid:31) are generated by a reward function1 r : O \u00d7 A \u2192 R if (cid:1). If the human\u2019s preferences are generated by a reward function r, then our agent ought to receive a high total reward according to r So if we know the reward function r, we can evaluate the agent quantitatively. Ideally the agent will achieve reward nearly as high as if it Qualitative: Sometimes we have no reward function by which we can quantitatively evaluate behavior (this is the situation where our approach would be practically useful). In these cases, all we can do is qualitatively evaluate how well the agent satis\ufb01es to the human\u2019s preferences. In this paper, we will start from a goal expressed in natural language, ask a human to evaluate the agent\u2019s behavior based on how well it ful\ufb01lls that goal, and then present videos of agents attempting to ful\ufb01ll that goal. ","13":" Our model based on trajectory segment comparisons is very similar to the trajectory preference queries used in Wilson et al (2012), except that we don\u2019t assume that we can reset the system to an arbitrary state2 and so our segments generally begin from different states. This complicates the interpretation of human comparisons, but we show that our algorithm overcomes this dif\ufb01culty even when the human raters have no understanding of our algorithm. At each point in time our method maintains a policy \u03c0 : O \u2192 A and a reward function estimate : O \u00d7 A \u2192 R, each parametrized by deep neural networks. The policy \u03c0 interacts with the environment to produce a set of trajectories {\u03c4 1, , \u03c4 i}. The parameters of \u03c0 are updated by a traditional reinforcement learning algorithm, in order to maximize the sum of the predicted rewards rt = \u02c6r(ot, at). ","14":" We select pairs of segments (cid:0)\u03c31, \u03c32(cid:1) from the trajectories {\u03c4 1, , \u03c4 i} produced in step 1, and send them to a human for comparison. The parameters of the mapping \u02c6r are optimized via supervised learning to \ufb01t the comparisons collected from the human so far. These processes run asynchronously, with trajectories \ufb02owing from process (1) to process (2), human comparisons \ufb02owing from process (2) to process (3), and parameters for \u02c6r \ufb02owing from process (3) to process (1). The following subsections provide details on each of these processes. After using \u02c6r to compute rewards, we are left with a traditional reinforcement learning problem. ","15":"We can solve this problem using any RL algorithm that is appropriate for the domain. One subtlety is that the reward function \u02c6r may be non-stationary, which leads us to prefer methods which are robust to changes in the reward function. This led us to focus on policy gradient methods, which have been applied successfully for such problems (Ho and Ermon, 2016). In this paper, we use advantage actor-critic (A2C; Mnih et al, 2016) to play Atari games, and trust region policy optimization (TRPO; Schulman et al, 2015) to perform simulated robotics tasks. In 1Here we assume here that the reward is a function of the observation and action. ","16":"In our experiments in Atari environments, we instead assume the reward is a function of the preceding 4 observations. In a general partially observable environment, we could instead consider reward functions that depend on the whole sequence of observations, and model this reward function with a recurrent neural network. 2Wilson et al (2012) also assumes the ability to sample reasonable initial states. But we work with high dimensional state spaces for which random states will not be reachable and the intended policy inhabits a low-dimensional manifold. each case, we used parameter settings which have been found to work well for traditional RL tasks. ","17":" The only hyperparameter which we adjusted was the entropy bonus for TRPO. This is because TRPO relies on the trust region to ensure adequate exploration, which can lead to inadequate exploration if the reward function is changing. We normalized the rewards produced by \u02c6r to have zero mean and constant standard deviation. This is typical preprocessing step which is particularly appropriate here since the position of the rewards is underdetermined by our learning problem. The human overseer is given a visualization of two trajectory segments, in the form of short movie clips. ","18":"In all of our experiments, these clips are between 1 and 2 seconds long. The human then indicates which segment they prefer, that the two segments are equally good, or that they are unable to compare the two segments. The human judgments are recorded in a database D of triples (cid:0)\u03c31, \u03c32, \u00b5(cid:1), where \u03c31 and \u03c32 are the two segments and \u00b5 is a distribution over {1, 2} indicating which segment the user preferred. If the human selects one segment as preferable, then \u00b5 puts all of its mass on that choice. If the human marks the segments as equally preferable, then \u00b5 is uniform. ","19":"Finally, if the human marks the segments as incomparable, then the comparison is not included in the database. We can interpret a reward function estimate \u02c6r as a preference-predictor if we view \u02c6r as a latent factor explaining the human\u2019s judgments and assume that the human\u2019s probability of preferring a segment \u03c3i depends exponentially on the value of the latent reward summed over the length of the clip:3 We choose \u02c6r to minimize the cross-entropy loss between these predictions and the actual human \u00b5(1) log \u02c6P (cid:2)\u03c31 (cid:31) \u03c32(cid:3) + \u00b5(2) log \u02c6P (cid:2)\u03c32 (cid:31) \u03c31(cid:3). This follows the Bradley-Terry model (Bradley and Terry, 1952) for estimating score functions from pairwise preferences, and is the specialization of the Luce-Shephard choice rule (Luce, 2005; Shepard, 1957) to preferences over trajectory segments. It can be understood as equating rewards with a preference ranking scale analogous to the famous Elo ranking system developed for chess (Elo, 1978). Just as the difference in Elo points of two chess players estimates the probability of one player defeating the other in a game of chess, the difference in predicted reward of two trajectory segments estimates the probability that one is chosen over the other by the human. ","20":" Our actual algorithm incorporates a number of modi\ufb01cations to this basic approach, which early experiments discovered to be helpful and which are analyzed in Section 33: We \ufb01t an ensemble of predictors, each trained on |D| triples sampled from D with replace- ment. The estimate \u02c6r is de\ufb01ned by independently normalizing each of these predictors and then averaging the results. A fraction of 1\/e of the data is held out to be used as a validation set for each predictor. We use (cid:96)2 regularization and adjust the regularization coef\ufb01cient to keep the validation loss between 11 and 15 times the training loss. In some domains we also apply dropout for regularization. ","21":" Rather than applying a softmax directly as described in Equation 1, we assume there is a 10% chance that the human responds uniformly at random. Conceptually this adjustment is needed because human raters have a constant probability of making an error, which doesn\u2019t decay to 0 as the difference in reward difference becomes extreme. 3Equation 1 does not use discounting, which could be interpreted as modeling the human to be indifferent about when things happen in the trajectory segment. Using explicit discounting or inferring the human\u2019s discount function would also be reasonable choices. We decide how to query preferences based on an approximation to the uncertainty in the reward function estimator, similar to Daniel et al (2014): we sample a large number of pairs of trajectory segments of length k, use each reward predictor in our ensemble to predict which segment will be preferred from each pair, and then select those trajectories for which the predictions have the highest variance across ensemble members. ","22":"This is a crude approximation and the ablation experiments in Section 3 show that in some tasks it actually impairs performance. Ideally, we would want to query based on the expected value of information of the query (Akrour et al, 2012; Krueger et al, 2016), but we leave it to future work to explore this direction further. We implemented our algorithm in TensorFlow (Abadi et al, 2016). We interface with Mu- JoCo (Todorov et al, 2012) and the Arcade Learning Environment (Bellemare et al, 2013) through the OpenAI Gym (Brockman et al, 2016). In our \ufb01rst set of experiments, we attempt to solve a range of benchmark tasks for deep RL without observing the true reward. ","23":"Instead, the agent learns about the goal of the task only by asking a human which of two trajectory segments is better. Our goal is to solve the task in a reasonable amount of time using as few queries as possible. In our experiments, feedback is provided by contractors who are given a 1-2 sentence description of each task before being asked to compare several hundred to several thousand pairs of trajectory segments for that task (see Appendix B for the exact instructions given to contractors). Each trajectory segment is between 1 and 2 seconds long. Contractors responded to the average query in 3-5 seconds, and so the experiments involving real human feedback required between 30 minutes and 5 hours of human time. ","24":" For comparison, we also run experiments using a synthetic oracle whose preferences over trajectories exactly re\ufb02ect reward in the underlying task. That is, when the agent queries for a comparison, instead of sending the query to a human, we immediately reply by indicating a preference for whichever trajectory segment actually receives a higher reward in the underlying task4. We also compare to the baseline of RL training using the real reward. Our aim here is not to outperform but rather to do nearly as well as RL without access to reward information and instead relying on much scarcer feedback. Nevertheless, note that feedback from real humans does have the potential to outperform RL (and as shown below it actually does so on some tasks), because the human feedback might provide a better-shaped reward. ","25":" We describe the details of our experiments in Appendix A, including model architectures, modi\ufb01ca- tions to the environment, and the RL algorithms used to optimize the policy. The \ufb01rst tasks we consider are eight simulated robotics tasks, implemented in MuJoCo (Todorov et al, 2012), and included in OpenAI Gym (Brockman et al, 2016). We made small modi\ufb01cations to these tasks in order to avoid encoding information about the task in the environment itself (the modi\ufb01cations are described in detail in Appendix A). The reward functions in these tasks are linear functions of distances, positions and velocities, and all are a quadratic function of the features. We included a simple cartpole task (\u201cpendulum\u201d) for comparison, since this is representative of the complexity of tasks studied in prior work. ","26":" Figure 2 shows the results of training our agent with 700 queries to a human rater, compared to learning from 350, 700, or 1400 synthetic queries, as well as to RL learning from the real reward. 4In the case of Atari games with sparse rewards, it is relatively common for two clips to both have zero reward in which case the oracle outputs indifference. Because we considered clips rather than individual states, such ties never made up a large majority of our data. Moreover, ties still provide signi\ufb01cant information to the reward predictor as long as they are not too common. Figure 2: Results on MuJoCo simulated robotics as measured on the tasks\u2019 true reward. ","27":"We compare our method using real human feedback (purple), our method using synthetic feedback provided by an oracle (shades of blue), and reinforcement learning using the true reward function (orange). All curves are the average of 5 runs, except for the real human feedback, which is a single run, and each point is the average reward over \ufb01ve consecutive batches. For Reacher and Cheetah feedback was provided by an author due to time constraints. For all other tasks, feedback was provided by contractors unfamiliar with the environments and with our algorithm. The irregular progress on Hopper is due to one contractor deviating from the typical labeling schedule. ","28":" With 700 labels we are able to nearly match reinforcement learning on all of these tasks. Training with learned reward functions tends to be less stable and higher variance, while having a comparable mean performance. Surprisingly, by 1400 labels our algorithm performs slightly better than if it had simply been given the true reward, perhaps because the learned reward function is slightly better shaped\u2014the reward learning procedure assigns positive rewards to all behaviors that are typically followed by high reward. Real human feedback is typically only slightly less effective than the synthetic feedback; depending on the task human feedback ranged from being half as ef\ufb01cient as ground truth feedback to being equally ef\ufb01cient. On the Ant task the human feedback signi\ufb01cantly outperformed the synthetic feedback, apparently because we asked humans to prefer trajectories where the robot was \u201cstanding upright,\u201d which proved to be useful reward shaping. ","29":"(There was a similar bonus in the RL reward function to encourage the robot to remain upright, but the simple hand-crafted bonus was not as useful. ) The second set of tasks we consider is a set of seven Atari games in the Arcade Learning Environ- ment (Bellemare et al, 2013), the same games presented in Mnih et al, 2013. Figure 3 shows the results of training our agent with 5,500 queries to a human rater, compared to learning from 350, 700, or 1400 synthetic queries, as well as to RL learning from the real reward. Our method has more dif\ufb01culty matching RL in these challenging environments, but nevertheless it displays substantial learning on most of them and matches or even exceeds RL on some. Speci\ufb01cally, on BeamRider and Pong, synthetic labels match or come close to RL even with only 3,300 such labels. ","30":"On Seaquest and Qbert synthetic feedback eventually performs near the level of RL but learns more slowly. On SpaceInvaders and Breakout synthetic feedback never matches RL, but nevertheless the agent improves substantially, often passing the \ufb01rst level in SpaceInvaders and reaching a score of 20 on Breakout, or 50 with enough labels. Figure 3: Results on Atari games as measured on the tasks\u2019 true reward. We compare our method using real human feedback (purple), our method using synthetic feedback provided by an oracle (shades of blue), and reinforcement learning using the true reward function (orange). All curves are the average of 3 runs, except for the real human feedback which is a single run, and each point is the average reward over about 150,000 consecutive frames. ","31":" Figure 4: Four frames from a single back\ufb02ip. The agent is trained to perform a sequence of back\ufb02ips, landing upright each time. The video is available at https:\/\/goo. gl\/MhgvIU. On most of the games real human feedback performs similar to or slightly worse than synthetic feedback with the same number of labels, and often comparably to synthetic feedback that has 40% fewer labels. ","32":"This may be due to human error in labeling, inconsistency between different contractors labeling the same run, or the uneven rate of labeling by contractors, which can cause labels to be overly concentrated in narrow parts of state space. The latter problems could potentially be addressed by future improvements to the pipeline for outsourcing labels. On Qbert, our method fails to learn to beat the \ufb01rst level with real human feedback; this may be because short clips in Qbert can be confusing and dif\ufb01cult to evaluate. Finally, Enduro is dif\ufb01cult for A3C to learn due to the dif\ufb01culty of successfully passing other cars through random exploration, and is correspondingly dif\ufb01cult to learn with synthetic labels, but human labelers tend to reward any progress towards passing cars, essentially shaping the reward and thus outperforming A3C in this game (the results are comparable to those achieved with DQN). Experiments with traditional RL tasks help us understand whether our method is effective, but the ultimate purpose of human interaction is to solve tasks for which no reward function is available. ","33":" Using the same parameters as in the previous experiments, we show that our algorithm can learn novel complex behaviors. We demonstrate: The Hopper robot performing a sequence of back\ufb02ips (see Figure 4). This behavior was trained using 900 queries in less than an hour. The agent learns to consistently perform a back\ufb02ip, land upright, and repeat. Figure 5: Performance of our algorithm on MuJoCo tasks after removing various components, as described in Section Section 33 All graphs are averaged over 5 runs, using 700 synthetic labels each. ","34":" The Half-Cheetah robot moving forward while standing on one leg. This behavior was trained using 800 queries in under an hour. Keeping alongside other cars in Enduro. This was trained with roughly 1,300 queries and 4 million frames of interaction with the environment; the agent learns to stay almost exactly even with other moving cars for a substantial fraction of the episode, although it gets confused by changes in background. Videos of these behaviors can be found at https:\/\/goo. ","35":"gl\/MhgvIU. These behaviors were trained using feedback from the authors. In order to better understand the performance of our algorithm, we consider a range of modi\ufb01cations: We pick queries uniformly at random rather than prioritizing queries for which there is disagreement (random queries). We train only one predictor rather than an ensemble (no ensemble). In this setting, we also choose queries at random, since there is no longer an ensemble that we could use to estimate disagreement. ","36":" We train on queries only gathered at the beginning of training, rather than gathered through- out training (no online queries). We remove the (cid:96)2 regularization and use only dropout (no regularization). On the robotics tasks only, we use trajectory segments of length 1 (no segments). Rather than \ufb01tting \u02c6r using comparisons, we consider an oracle which provides the true total reward over a trajectory segment, and \ufb01t \u02c6r to these total rewards using mean squared error (target). The results are presented in Figure 5 for MuJoCo and Figure 6 for Atari. ","37":" Of particular interest is the poor performance of of\ufb02ine reward predictor training; here we \ufb01nd that due to the nonstationarity of the occupancy distribution, the predictor captures only part of the true reward, and maximizing this partial reward can lead to bizarre behavior that is undesirable as measured by the true reward (Amodei et al, 2016). For instance, on Pong of\ufb02ine training sometimes leads our agent to avoid losing points but not to score points; this can result in extremely long volleys Figure 6: Performance of our algorithm on Atari tasks after removing various components, as described in Section 33 All curves are an average of 3 runs using 5,500 synthetic labels (see minor exceptions in Section A2). that repeat the same sequence of events ad in\ufb01nitum (videos at https:\/\/goo. gl\/L5eAbk). This type of behavior demonstrates that in general human feedback needs to be intertwined with RL learning rather than provided statically. ","38":" Our main motivation for eliciting comparisons rather than absolute scores was that we found it much easier for humans to provide consistent comparisons than consistent absolute scores, especially on the continuous control tasks and on the qualitative tasks in Section 32; nevertheless it seems important to understand how using comparisons affects performance. For continuous control tasks we found that predicting comparisons worked much better than predicting scores. This is likely because the scale of rewards varies substantially and this complicates the regression problem, which is smoothed signi\ufb01cantly when we only need to predict comparisons. In the Atari tasks we clipped rewards and effectively only predicted the sign, avoiding these dif\ufb01culties (this is not a suitable solution for the continuous control tasks because the relative magnitude of the reward are important to learning). In these tasks comparisons and targets had signi\ufb01cantly different performance, but neither consistently outperformed the other. ","39":" We also observed large performance differences when using single frames rather than clips5. In order to obtain the same results using single frames we would need to have collected signi\ufb01cantly more comparisons. In general we discovered that asking humans to compare longer clips was signi\ufb01cantly more helpful per clip, and signi\ufb01cantly less helpful per frame. We found that for short clips it took human raters a while just to understand the situation, while for longer clips the evaluation time was roughly linear function of the clip length. We tried to choose the shortest clip length for which the evaluation time was linear. ","40":"In the Atari environments we also found that it was often easier to compare longer clips because they provide more context than single frames. Agent-environment interactions are often radically cheaper than human interaction. We show that by learning a separate reward model using supervised learning, it is possible to reduce the interaction complexity by roughly 3 orders of magnitude. Not only does this show that we can meaningfully train deep RL agents from human preferences, but also that we are already hitting diminishing returns 5We only ran these tests on continuous control tasks because our Atari reward model depends on a sequence of consecutive frames rather than a single frame, as described in Section A2 on further sample-complexity improvements because the cost of compute is already comparable to the cost of non-expert feedback. 6 Although there is a large literature on preference elicitation and reinforcement learning from unknown reward functions, we provide the \ufb01rst evidence that these techniques can be economically scaled up to state-of-the-art reinforcement learning systems. ","41":"This represents a step towards practical applications of deep RL to complex real-world tasks. Future work may be able to improve the ef\ufb01ciency of learning from human preferences, and expand the range of tasks to which it can be applied. In the long run it would be desirable to make learning a task from human preferences no more dif\ufb01cult than learning it from a programmatic reward signal, ensuring that powerful RL systems can be applied in the service of complex human values rather than low-complexity goals. We thank Olivier Pietquin, Bilal Piot, Laurent Orseau, Pedro Ortega, Victoria Krakovna, Owain Evans, Andrej Karpathy, Igor Mordatch, and Jack Clark for reading drafts of the paper. We thank Tyler Adkisson, Mandy Beri, Jessica Richards, Heather Tran, and other contractors for providing the data used to train our agents. ","42":"Abstract For sophisticated reinforcement learning (RL) systems to interact usefully with real-world environments, we need to communicate complex goals to these systems. In this work, we explore goals de\ufb01ned in terms of (non-expert) human preferences between pairs of trajectory segments. We show that this approach can effectively solve complex RL tasks without access to the reward function, including Atari games and simulated robot locomotion, while providing feedback on less than 1% of our agent\u2019s interactions with the environment. This reduces the cost of human oversight far enough that it can be practically applied to state-of-the-art RL systems. To demonstrate the \ufb02exibility of our approach, we show that we can successfully train complex novel behaviors with about an hour of human time. These behaviors and environments are considerably more complex than any which have been previously learned from human feedback. ","43":"Large-scale language model pretraining has become increasingly prevalent for achieving high per- formance on a variety of natural language processing (NLP) tasks. When applying these models to a speci\ufb01c task, they are usually \ufb01ne-tuned using supervised learning, often to maximize the log probability of a set of human demonstrations. While this strategy has led to markedly improved performance, there is still a misalignment between this \ufb01ne-tuning objective\u2014maximizing the likelihood of human-written text\u2014and what we care about\u2014generating high-quality outputs as determined by humans. This misalignment has several causes: the maximum likelihood objective has no distinction between important errors (eg making up facts ) and unimportant errors (eg selecting the precise word from a set of synonyms); models \u2217This was a joint project of the OpenAI Re\ufb02ection team. Author order was randomized amongst {LO, JW, DZ, NS}; CV and RL were full-time contributors for most of the duration. ","44":"PC is the team lead. 2Samples from all of our models can be viewed on our website. 3We provide inference code for our 13B models and baselines, as well as a model card and our human feedback dataset with over 64k summary comparisons, here. 34th Conference on Neural Information Processing Systems (NeurIPS 2020), Vancouver, Canada. Figure 1: Fraction of the time humans prefer our models\u2019 summaries over the human-generated reference summaries on the TL;DR dataset. ","45":"4Since quality judgments involve an arbitrary decision about how to trade off summary length vs. coverage within the 24-48 token limit, we also provide length-controlled graphs in Appendix F; length differences explain about a third of the gap between feedback and supervised learning at 67B. are incentivized to place probability mass on all human demonstrations, including those that are low-quality; and distributional shift during sampling can degrade performance . Quality can often be improved signi\ufb01cantly by non-uniform sampling strategies such as beam search , but these can lead to repetition and other undesirable artifacts . Optimizing for quality may be a principled approach to overcoming these problems. ","46":" Our goal in this paper is to advance methods for training language models on objectives that more closely capture the behavior we care about. To make short-term progress towards this goal, we focus on abstractive English text summarization, as it has a long history in the NLP community and is a subjective task where we believe it is dif\ufb01cult to quantify summary quality without human judgments. Indeed, existing automatic metrics for evaluating summary quality, such as ROUGE , have received criticism for poor correlation with human judgments . We follow the works of , who \ufb01ne-tune language models from human feedback using reward learning . We \ufb01rst collect a dataset of human preferences between pairs of summaries, then train reward model (RM) via supervised learning to predict the human-preferred summary. ","47":"Finally, we train a policy via reinforcement learning (RL) to maximize the score given by the RM; the policy generates a token of text at each \u2018time step\u2019, and is updated using the PPO algorithm based on the RM \u2018reward\u2019 given to the entire generated summary. We can then gather more human data using samples from the resulting policy, and repeat the process. We follow the works of and use large pretrained GPT-3 models with as many as 67 billion parameters. Our main contributions are four-fold. (1) We show that training with human feedback signi\ufb01cantly outperforms very strong baselines on English summarization. ","48":"When applying our methods on a version of the Reddit TL;DR dataset we train policies via human feedback that produce better summaries than much larger policies trained via supervised learning. Summaries from our human feedback models are preferred by our labelers to the original human demonstrations in the dataset (see Figure 1). (2) We show human feedback models generalize much better to new domains than supervised models. Our Reddit-trained human feedback models also generate high-quality summaries of news articles on the CNN\/DailyMail (CNN\/DM) dataset without any news-speci\ufb01c \ufb01ne-tuning, almost matching the quality of the dataset\u2019s reference summaries. We perform several checks to ensure that these human preferences re\ufb02ect a real quality difference: we consistently monitor agreement rates amongst labelers and researchers, and \ufb01nd researcher-labeler agreement rates are nearly as high as researcher-researcher agreement rates (see Section C2), and we verify models are not merely optimizing simple metrics like length or amount of copying (see Appendices F and G7). ","49":" 4Throughout the paper, error bars represent 1 standard error. (3) We conduct extensive empirical analyses of our policy and reward model. We examine the impact of model and data size (Figure 6), study performance as we continue to optimize a given reward model (Section 43), and analyze reward model performance using synthetic and human- written perturbations of summaries (Section 43). We con\ufb01rm that our reward model outperforms other metrics such as ROUGE at predicting human preferences, and that optimizing our reward model directly results in better summaries than optimizing ROUGE according to humans (Section 44). (4) We publicly release our human feedback dataset for further research. ","50":"The dataset contains 64,832 summary comparisons on the TL;DR dataset, as well as our evaluation data on both TL;DR (comparisons and Likert scores) and CNN\/DM (Likert scores). The methods we present in this paper are motivated in part by longer-term concerns about the misalignment of AI systems with what humans want them to do. When misaligned summarization models make up facts, their mistakes are fairly low-risk and easy to spot. However, as AI systems become more powerful and are given increasingly important tasks, the mistakes they make will likely become more subtle and safety-critical, making this an important area for further research. Most directly related to our work is previous work using human feedback to train summarization models with RL . ","51":"Bohm et al learn a reward function from a dataset of human ratings of 25k CNN\/DM summaries, and train a policy whose summaries are preferred to a policy optimizing ROUGE. Our work is most similar to , who also train Transformer models to optimize human feedback across a range of tasks, including summarization on the Reddit TL;DR and CNN\/DM datasets. Unlike us, they train in an online manner and \ufb01nd the model highly extractive. They note that their labelers prefer extractive summaries and have low agreement rates with researchers. Compared to , we use signi\ufb01cantly larger models, move to the batch setting for collecting human feedback, ensure high labeler-researcher agreement, and make some algorithmic modi\ufb01cations, such as separating the policy and value networks. ","52":" Human feedback has also been used as a reward to train models in other domains such as dialogue translation , semantic parsing , story generation , review generation and evidence extraction . Our reward modeling approach was developed in prior work on learning to rank , which has been applied to ranking search results using either explicit feedback or implicit feedback in the form of click-through data . In a related line of research, human feedback has been used to train agents in simulated environments . There is also a rich literature on using RL to optimize automatic metrics for NLP tasks, such as ROUGE for summarization , BLEU for translation , and other domains . Finally, there has been extensive research on modifying architectures and pre-training procedures for improving summarization performance. ","53":" Our approach is similar to the one outlined in , adapted to the batch setting. We start with an initial policy that is \ufb01ne-tuned via supervised learning on the desired dataset (in our case, the Reddit TL;DR summarization dataset). The process (illustrated in Figure 2) then consists of three steps that can be repeated iteratively. Step 1: Collect samples from existing policies and send comparisons to humans. For each Reddit post, we sample summaries from several sources including the current policy, initial policy, original reference summaries and various baselines. ","54":"We send a batch of pairs of summaries to our human evaluators, who are tasked with selecting the best summary of a given Reddit post. Step 2: Learn a reward model from human comparisons. Given a post and a candidate summary, we train a reward model to predict the log odds that this summary is the better one, as judged by our labelers. Step 3: Optimize a policy against the reward model. We treat the logit output of the reward model as a reward that we optimize using reinforcement learning, speci\ufb01cally with the PPO algorithm . ","55":" Figure 2: Diagram of our human feedback, reward model training, and policy training procedure. We provide a more thorough description of our procedure, including details of the reward model and policy training and our quality control process, in the following sections. In practice, rather than precisely iterating this sequence of three steps, we updated our data collection and training procedures over the course of the project while accumulating labels (see Appendix C6 for details). Datasets. We use the TL;DR summarization dataset , which contains ~3 million posts from reddit. ","56":"com across a variety of topics (subreddits), as well summaries of the posts written by the original poster (TL;DRs). We additionally \ufb01lter this dataset (see Appendix A) to ensure quality, including using a whitelist of subreddits that are understandable to the general population. Crucially, we also \ufb01lter to include only posts where the human-written summaries contain between 24 and 48 tokens, to minimize the potential effect of summary length on quality (see Section 41 and Appendix F). Our \ufb01nal \ufb01ltered dataset contains 123,169 posts, and we hold out ~5% as a validation set. For the remainder of this paper, we refer to this dataset simply as TL;DR. ","57":" We chose the TL;DR dataset over the more commonly used CNN\/DM dataset primarily because very strong performance can be attained on CNN\/DM with simple extractive baselines. We \ufb01nd in Section 42 that our labelers prefer lead-3 over the CNN\/DM reference summaries,5 and that the supervised T5 model with low-temperature sampling already surpasses the reference summary quality, while copying extensively from the article. On the other hand, simple extractive baselines perform poorly on TL;DR in our human evaluations (see Appendix G2). Instead of training on CNN\/DM, we study the transfer performance of our human feedback models to CNN\/DM after being trained to summarize Reddit posts. Task. ","58":"We de\ufb01ne our ground-truth task as producing a model that generates summaries fewer than 48 tokens long that are as good as possible, according to our judgments. We judge summary quality by how faithfully the summary conveys the original post to a reader who can only read the summary and not the post (see Appendix C5 for further discussion of criteria). Since we have limited capacity to do comparisons, we hire labelers to do the comparisons for us. We rely on detailed procedures to ensure high agreement between labelers and us on the task, which we describe in the next section. 5We manually check this result in Appendix E and \ufb01nd we generally agree with labeler ratings. ","59":" TL;DR dataset. dataset. model. post. summaries. ","60":" evaluation. post. model. PPO. First date ever, going to the beach. ","61":"Would like some tips Hey Reddit! I (20M) would like some tips, because I have my \ufb01rst ever date tomorrow (although I\u2019ve had a gf for 3 years, but no actual dating happened), and we\u2019re going to the beach. met this girl, we have mutual friends, at a festival a few days ago. We didn\u2019t kiss, but we talked, held hands, danced a bit. I asked her to go on a date with me, which was super hard as it is the \ufb01rst time I\u2019ve asked this to anybody. What I mean to say is, it\u2019s not like a standard *\ufb01rst* date because we already spent some time together. ","62":" I\u2019m really nervous and excited. I\u2019m going to pick her up tomorrow, we\u2019re cycling to the beach which will take 30 minutes, and then what? I\u2019m a bit scared. Should I bring something (the weather, although no rain and sunny, is not super so no swimming), should we do something. I\u2019d like all the tips I can get. Thanks! ship, going to the beach, terri\ufb01ed. ","63":" few days ago, going to the beach. row, cycling to the beach. Would bring. I\u2019m a bit nervous and excited. 67B human feedback model Going on my \ufb01rst ever date tomor- row, cycling to the beach. ","64":"Would like some tips on what to do and bring. I\u2019m a bit nervous and excited. Table 1: Example of post and samples on the TL;DR dataset, chosen to be particularly short. For random samples (along with posts), see Appendix H and our website. Previous work on \ufb01ne-tuning language models from human feedback reported \u201ca mismatch between the notion of quality we wanted our model to learn, and what the humans labelers actually evaluated\u201d, leading to model-generated summaries that were high-quality according to the labelers, but fairly low-quality according to the researchers. ","65":" Compared to , we implement two changes to improve human data quality. First, we transition entirely to the of\ufb02ine setting, where we alternate between sending large batches of comparison data6 to our human labelers and re-training our models on the cumulative collected data. Second, we maintain a hands-on relationship with labelers:7 we on-board them with detailed instructions, answer their questions in a shared chat room, and provide regular feedback on their performance. We train all labelers to ensure high agreement with our judgments, and continuously monitor labeler-researcher agreement over the course of the project. See Appendix C1 and C5 for details. ","66":" As a result of our procedure, we obtained high labeler-researcher agreement: on a subset of compari- son tasks, labelers agree with researchers 77% \u00b1 2% of the time, while researchers agree with each other 73% \u00b1 4% of the time. We provide more analysis of our human data quality in Appendix C2 All of our models are Transformer decoders in the style of GPT-3 . We conduct our human feedback experiments on models with 13 billion (13B) and 67 billion (67B) parameters. Pretrained models. Similarly to , we start with models pretrained to autoregressively predict the next token in a large text corpus. ","67":"As in , we use these models as \u2018zero-shot\u2019 baselines by padding the context with examples of high-quality summaries from the dataset. We provide details on pretraining in Appendix B, and on our zero-shot procedure in Appendix B2 Supervised baselines. We next \ufb01ne-tune these models via supervised learning to predict summaries from our \ufb01ltered TL;DR dataset (see Appendix B for details). We use these supervised models to sample initial summaries for collecting comparisons, to initialize our policy and reward models, and as baselines for evaluation. In our \ufb01nal human evaluations, we use T=0 to sample from all models, as we found it performed better than higher temperatures or nucleus sampling (see Appendix B1). ","68":" To validate that our supervised models are indeed strong baselines for comparison, we run our supervised \ufb01ne-tuning procedure with our 67B model on the CNN\/DM dataset, and \ufb01nd that we achieve slightly better ROUGE scores than SOTA models from mid-2019 (see Appendix G4). 6Our decision to collect comparisons rather than Likert scores is supported by recent work, eg . 7We recruited labelers from a freelancing platform, Upwork, and two labeling services, Scale and Lionbridge. Reward models. To train our reward models, we start from a supervised baseline, as described above, then add a randomly initialized linear head that outputs a scalar value. ","69":"We train this model to predict which summary y \u2208 {y0, y1} is better as judged by a human, given a post x If the summary preferred by the human is yi, we can write the RM loss as: where r\u03b8(x, y) is the scalar output of the reward model for post x and summary y with parameters \u03b8, and D is the dataset of human judgments. At the end of training, we normalize the reward model outputs such that the reference summaries from our dataset achieve a mean score of 0 Human feedback policies. We want to use the reward model trained above to train a policy that generates higher-quality outputs as judged by humans. We primarily do this using reinforcement learning, by treating the output of the reward model as a reward for the entire summary that we maximize with the PPO algorithm , where each time step is a BPE token. We initialize our policy to be the model \ufb01ne-tuned on Reddit TL;DR. ","70":"Importantly, we include a term in the reward that penalizes the KL divergence between the learned RL policy \u03c0RL supervised model \u03c0SFT, as previously done in . The full reward R can be written as: This KL term serves two purposes. First, it acts as an entropy bonus, encouraging the policy to explore and deterring it from collapsing to a single mode. Second, it ensures the policy doesn\u2019t learn to produce outputs that are too different from those that the reward model has seen during training. For the PPO value function, we use a Transformer with completely separate parameters from the policy. ","71":"This prevents updates to the value function from partially destroying the pretrained policy early in training (see ablation in Appendix G1). We initialize the value function to the parameters of the reward model. In our experiments, the reward model, policy, and value function are the same size. Policies trained with human feedback are preferred to much larger supervised policies. Our main results evaluating our human feedback policies on TL;DR are shown in Figure 1 We measure policy quality as the percentage of summaries generated by that policy that humans prefer over the reference summaries in the dataset. ","72":"Our policies trained with human feedback signi\ufb01cantly outperform our supervised baselines on this metric, with our 13B human feedback model signi\ufb01cantly outperforming a supervised model 10\u00d7 its size (61% versus 43% raw preference score against reference summaries). Our 67B model in turn signi\ufb01cantly outperforms our 13B model, suggesting that training with human feedback also bene\ufb01ts from scale. Additionally, both of our human feedback models are judged by humans to be superior to the human demonstrations used in the dataset. Controlling for summary length. When judging summary quality, summary length is a confound- ing factor. ","73":"The target length of a summary is implicitly part of the summarization task; depending on the desired trade-off between conciseness and coverage, a shorter or longer summary might be better. Since our models learned to generate longer summaries, length could account for much of our quality improvements. We \ufb01nd that after controlling for length (Appendix F), the preference of our human feedback models vs. reference summaries drops by ~5%; even so, our 67B model summaries are still preferred to the reference summaries ~65% of the time. How do our policies improve over the baselines? To better understand the quality of our models\u2019 summaries compared to the reference summaries and those of our supervised baselines, we conduct an additional analysis where human labelers assess summary quality across four dimensions (or \u201caxes\u201d) using a 7-point Likert scale . ","74":"Labelers rated summaries for coverage (how much important information from the original post is covered), accuracy (to what degree the statements in the summary are stated in the post), coherence (how easy the summary is to read on its own), and overall quality. 8Note that the reward model only gives rewards for entire summaries, and not at intermediate time steps. In RL terminology, each episode terminates when the policy outputs the EOS token, and the discount factor \u03b3 = 1 Figure 4: Transfer results on CNN\/DM. (a) Overall summary quality on CNN\/DM as a function of model size. Full results across axes shown in Appendix G2 (b) Overall scores vs. ","75":"length for the 67B TL;DR supervised baseline, the 67B TL;DR human feedback model, and T5 \ufb01ne-tuned on CNN\/DM summaries. At similar summary lengths, our 67B TL;DR human feedback model nearly matches T5 despite never being trained to summarize news articles. dimension of quality, but particularly coverage. Although our human labelers had a high bar for giving perfect overall scores, summaries from our 67B PPO model achieve a 7\/7 overall score 45% of the time (compared to 20% and 23% respectively). training (Figure 4). ","76":"Our human feedback models signi\ufb01- on TL;DR and models trained only on pretraining corpora. In fact, our 67B human feedback model performs almost as well as a 67B model that was \ufb01ne-tuned on the CNN\/DM reference summaries, despite generating much shorter summaries. summary quality on the TL;DR dataset. Since our human feedback models transferred to CNN\/DM have little overlap in summary length distribution with models trained on CNN\/DM, with about half as many tokens on average, they are dif\ufb01cult to compare directly. Thus our evaluations in Figure 4 use a 7-point Likert scale on four quality dimensions, as in Section 41 (see Appendix C5 for labeler instructions). ","77":"In Figure 4b we show the average overall score at different summary lengths, which suggests our human feedback models would perform even better if they generated longer summaries. Qualitatively, CNN\/DM summaries from our human feedback models are consistently \ufb02uent and reasonable representations of the article; we show examples on our website and in Appendix H What happens as we optimize the reward model? Optimizing against our reward model is supposed to make our policy align with human preferences. But the reward model isn\u2019t a perfect representation of our labeler preferences, as it has limited capacity and only sees a small amount of comparison data from a relatively narrow distribution of summaries. While we can hope our reward model generalizes to summaries unseen during training, it\u2019s unclear how much one can optimize against the reward model until it starts giving useless evaluations. To answer this question, we created a range of policies optimized against an earlier version of our reward model, with varying degrees of optimization strength, and asked labelers to compare samples from them to the reference summaries. ","78":"Figure 5 shows the results for PPO at a range of KL penalty reward model optimization. Optimizing against but eventually over\ufb01ts, giving worse summaries. model (see rm3 in Appendix C6). See Appendix H2 for samples from the KL 250 model. data size and model size. ","79":"Doubling amount of the model size leads to a ~18% increase. The ing the accuracy of a single human. coef\ufb01cients (\u03b2). Under light optimization, the models improve (according to labelers). However, as we optimize further, true preferences fall off compared to the prediction, and eventually the reward model becomes anti-correlated with human preferences. ","80":"Though this is clearly undesirable, we note that this over-optimization also happens with ROUGE (see and Appendix G3). Similar behavior has been observed in learned reward functions in the robotics domain . How does reward modeling scale with increasing model and data size? We conduct an ablation to determine how data quantity and model size affect reward modeling performance. We train 7 reward models ranging from 160M to 13B parameters, on 8k to 64k human comparisons from our dataset. We \ufb01nd that doubling the training data amount leads to a ~11% increase in the reward model validation set accuracy, whereas doubling the model size leads to a ~18% increase (Figure 6). ","81":" What has the reward model learned? We probe our reward model by evaluating it on several validation sets. We show the full results in Appendix G6, and highlight them here. We \ufb01nd that our reward models generalize to evaluating CNN\/DM summaries (Appendix G7), agreeing with labeler preferences 62. 4% and 66. 5% of the time (for our 13B and 67B models, respectively). ","82":"Our 67B reward model nearly matches the inter-labeler agreement value of 66. 9%. We also \ufb01nd that our reward models are sensitive to small but semantically important details in the summary. We construct an additional validation set by having labelers make minimal edits to summaries to improve them. Our RMs prefer the edited summaries almost as often (79. ","83":"4% for 13B and 82. 8% for 67B) as a separate set of human evaluators (84. 1%) Further, when comparing the reference summaries to perturbed summaries where the participants\u2019 roles are reversed, our models reliably select the original summary (92. 9% of the time for 13B, 97. 2% for 67B). ","84":"However, our RMs are biased towards longer summaries: our 67B RM prefers improving edits that make the summary shorter only 62. 6% of the time (vs. 76. 4% for humans). Evaluation. ","85":"We study how well various automatic metrics act as predictors for human preferences, and compare them to our RMs. Speci\ufb01cally, we examine ROUGE, summary length, amount of copying from the post,9 and log probability under our baseline supervised models. We present a full matrix of agreement rates between these metrics in Appendix G7 We \ufb01nd that our learned reward models consistently outperform other metrics, even on the CNN\/DM dataset on which it was never trained. We also \ufb01nd that ROUGE fails to track sample quality as our 9We measure copying by computing the longest common subsequence of bigrams with the original Reddit post or news article, and dividing by the number of bigrams in the summary. Figure 7: Summary quality as a function of metric optimized and amount of optimization, using best-of-N rejection sampling. ","86":"We evaluate ROUGE, our main reward models, and an earlier iteration of the 13B model trained on approximately 75% as much data (see Table 11 for details). ROUGE appears to peak both sooner and at a substantially lower preference rate than all reward models. models improve. While ROUGE has ~57% agreement with labelers when comparing samples from our supervised baseline models, this drops to ~50% for samples from our human feedback model. Similarly, log probability agreement with humans drops to \u226450% on comparisons between samples from our human feedback models, while our RMs still perform above chance (62%) Scaling up the size of the supervised model does not reliably improve log probability\u2019s agreement with labelers. ","87":" Optimization. In Figure 7, we show that optimizing ROUGE using a simple optimization scheme doesn\u2019t consistently increase quality, as has been noted in . Optimization against ROUGE peaks both sooner and at a substantially lower quality rate than optimization against our reward models. Limitations. One limitation of our work is the time and cost required to produce our \ufb01nal models. ","88":" Notably, \ufb01ne-tuning our 67B model with RL required approximately 320 GPU-days. Our data collection procedure is also expensive compared to prior work \u2014 the training set took thousands of labeler hours and required signi\ufb01cant researcher time to ensure quality. For this reason, we were unable to collect baselines such as an equivalent amount of high-quality human demonstrations for supervised baselines. See D for more discussion. We leave this ablation to future work. ","89":"Nevertheless, we believe reward modeling is more likely to scale to tasks where it is extremely skill-intensive or time-consuming to provide good demonstrations. Future directions. The methods in this paper could be applied to any task where humans can compare samples, including dialogue, machine translation, question answering, speech synthesis, and music generation. We expect this method to be particularly important for generating long samples, where the distributional shift and degeneracy of maximum likelihood samples can be problematic. It may be possible to improve sample ef\ufb01ciency by training to predict feedback across many tasks . ","90":" We are particularly interested in scaling human feedback to tasks where humans can\u2019t easily evaluate the quality of model outputs. In this setting, it is particularly challenging to identify whether an ML system is aligned with the human designer\u2019s intentions. One approach is to train ML systems to help humans perform the evaluation task quickly and accurately . There is also a rich landscape of human feedback methods beyond binary comparisons that could be explored for training models . For example, we could solicit high-quality demonstra- tions from labelers, have labelers edit model outputs to make them better, or have labelers provide explanations for why they preferred one model output over another. ","91":"All of this feedback could be leveraged as a signal to train more capable reward models and policies. Broader impacts. The techniques we explore in this paper are generic techniques that could be used in a wide variety of machine learning applications, for any task where it is feasible for humans to evaluate the quality of model outputs. Thus, the potential implications are quite broad. Our research is primarily motivated by the potential positive effects of aligning machine learning algorithms with the designer\u2019s preferences. ","92":"Many machine learning applications optimize simple metrics which are only rough proxies for what the designer intends. This can lead to problems, such as Youtube recommendations promoting click-bait . In the short term, improving techniques for learning from and optimizing human preferences directly may enable these applications to be more aligned with human well-being. In the long term, as machine learning systems become more capable it will likely become increasingly dif\ufb01cult to ensure that they are behaving safely: the mistakes they make might be more dif\ufb01cult to spot, and the consequences will be more severe. For instance, writing an inaccurate summary of a news article is both easy to notice (one simply has to read the original article) and has fairly low consequences. ","93":"On the other hand, imitating human driving may be substantially less safe than driving to optimize human preferences. We believe that the techniques we explore in this paper are promising steps towards mitigating the risks from such capable systems, and better aligning them with what humans care about. Unfortunately, our techniques also enable malicious actors to more easily train models that cause societal harm. For instance, one could use human feedback to \ufb01ne-tune a language model to be more persuasive and manipulate humans\u2019 beliefs, or to induce dependence of humans on the technology, or to generate large amounts of toxic or hurtful content intended to harm speci\ufb01c individuals. Avoiding these outcomes is a signi\ufb01cant challenge for which there are few obvious solutions. ","94":" Large-scale models trained with human feedback could have signi\ufb01cant impacts on many groups. Thus, it is important to be careful about how we de\ufb01ne the \u2018good\u2019 model behavior that human labelers will reinforce. Deciding what makes a good summary is fairly straightforward, but doing this for tasks with more complex objectives, where different humans might disagree on the correct model behavior, will require signi\ufb01cant care. In these cases, it is likely not appropriate to use researcher labels as the \u2018gold standard\u2019; rather, individuals from groups impacted by the technology should be included in the process to de\ufb01ne \u2018good\u2019 behavior, and hired as labelers to reinforce this behavior in the model. We chose to train on the Reddit TL;DR dataset because the summarization task is signi\ufb01cantly more challenging than on CNN\/DM. ","95":"However, since the dataset consists of user-submitted posts with minimal moderation, they often contain content that is offensive or re\ufb02ects harmful social biases. This means our models can generate biased or offensive summaries, as they have been trained to summarize such content. For this reason, we recommend that the potential harms of our models be thoroughly studied before deploying them in user-facing applications. Finally, by improving the ability of machine learning algorithms to perform tasks that were previously only achievable by humans, we are increasing the likelihood of many jobs being automated, potentially leading to signi\ufb01cant job loss. Without suitable policies targeted at mitigating the effects of large-scale unemployment, this could also lead to signi\ufb01cant societal harm. ","96":"Abstract As language models become more powerful, training and evaluation are increas- ingly bottlenecked by the data and metrics used for a particular task. For example, summarization models are often trained to predict human reference summaries and evaluated using ROUGE, but both of these metrics are rough proxies for what we really care about\u2014summary quality. In this work, we show that it is possible to signi\ufb01cantly improve summary quality by training a model to optimize for human preferences. We collect a large, high-quality dataset of human comparisons be- tween summaries, train a model to predict the human-preferred summary, and use that model as a reward function to \ufb01ne-tune a summarization policy using reinforce- ment learning. We apply our method to a version of the TL;DR dataset of Reddit posts [63] and \ufb01nd that our models signi\ufb01cantly outperform both human reference summaries and much larger models \ufb01ne-tuned with supervised learning alone. Our models also transfer to CNN\/DM news articles [22], producing summaries nearly as good as the human reference without any news-speci\ufb01c \ufb01ne-tuning.2 We con- duct extensive analyses to understand our human feedback dataset and \ufb01ne-tuned models.3 We establish that our reward model generalizes to new datasets, and that optimizing our reward model results in better summaries than optimizing ROUGE according to humans. We hope the evidence from our paper motivates machine learning researchers to pay closer attention to how their training loss affects the model behavior they actually want. ","97":"Conversational agents, chatbots, dialogue systems and virtual assistants are some of the terms used by scientiic\nliterature to describe software-based systems which are capable of processing natural language data to\nsimulate a smart conversational process with humans . These conversational mechanisms are built and driven\nby a wide variety of techniques of diferent complexity, from traditional, pre-coded algorithms to emerging\nadaptive machine learning algorithms . Usually deployed as service-oriented systems, they are designed to\nassist users to achieve a speciic goal based on their personal needs . To this end, they autonomously generate\nnatural language messages to interact and communicate with users by emulating a real human being . \nAlthough the interest in both industry and research has dramatically increased in recent years , the\nstudy of natural language communication between human beings and machines is indeed not a novel concept. ","98":"\nELIZA , which has been historically considered as the irst chatbot, was designed and developed by the\nMassachusetts Institute of Technology (MIT) more than half a century ago, between 1964 and 1966. Alongside\nsuccessive chatbots like PARRY , these innovative systems laid the groundwork for specialized research in\nthe ield of human-computer interaction (HCI), focusing on the social and communicative perspectives and their\nimpact on the design and development of these systems. Recent advances in the ield of artiicial intelligence have brought back attention to the potential of conversational agents, especially with the emergence of machine and\ndeep learning techniques . Furthermore, specialized research ields such as natural language understanding\n(NLU), natural language generation (NLG) and dialogue stage tracking (DST) have become disruptive areas by\nintroducing innovative, eicient and accurate solutions to machine cognitive problems . \n. ","99":"Abstract A conversational information retrieval (CIR) system is an information retrieval (IR) system with a conversational interface which allows users to interact with the system to seek information via multi-turn conversations of natural language, in spoken or written form. Recent progress in deep learning has brought tremendous improvements in natural language processing (NLP) and conversational AI, leading to a plethora of commercial conversational services that allow naturally spoken and typed interaction, increasing the need for more human-centric interactions in IR. As a result, we have witnessed a resurgent interest in developing modern CIR systems in both research communities and industry. This book surveys recent advances in CIR, focusing on neural approaches that have been developed in the last few years. This book is based on the authors\u2019 tutorial at SIGIR\u20192020 [Gao et al., 2020b], with IR and NLP communities as the primary target audience. However, audiences with other background, such as machine learning and human-computer interaction, will also \ufb01nd it an accessible introduction to CIR. We hope that this book will prove a valuable resource for students, researchers, and software developers. This manuscript is a working draft. Comments are welcome. ","100":"Pre-trained neural language models have been shown to learn a substantial amount of in-depth knowl- edge from data . They can do so without any access to an external memory, as a parameterized implicit knowledge base . While this development is exciting, such models do have down- sides: They cannot easily expand or revise their memory, can\u2019t straightforwardly provide insight into their predictions, and may produce \u201challucinations\u201d . Hybrid models that combine parametric memory with non-parametric (ie, retrieval-based) memories can address some of these issues because knowledge can be directly revised and expanded, and accessed knowledge can be inspected and interpreted. REALM and ORQA , two recently introduced models that combine masked language models with a differentiable retriever, have shown promising results, Figure 1: Overview of our approach. ","101":"We combine a pre-trained retriever (Query Encoder + Document Index) with a pre-trained seq2seq model (Generator) and \ufb01ne-tune end-to-end. For query x, we use Maximum Inner Product Search (MIPS) to \ufb01nd the top-K documents zi. For \ufb01nal prediction y, we treat z as a latent variable and marginalize over seq2seq predictions given different documents. but have only explored open-domain extractive question answering. Here, we bring hybrid parametric and non-parametric memory to the \u201cworkhorse of NLP,\u201d ie sequence-to-sequence (seq2seq) models. ","102":" We endow pre-trained, parametric-memory generation models with a non-parametric memory through general-purpose \ufb01ne-tuning approach which we refer to as retrieval-augmented generation (RAG). We build RAG models where the parametric memory is a pre-trained seq2seq transformer, and the non-parametric memory is a dense vector index of Wikipedia, accessed with a pre-trained neural retriever. We combine these components in a probabilistic model trained end-to-end (Fig. 1). The retriever (Dense Passage Retriever , henceforth DPR) provides latent documents conditioned on the input, and the seq2seq model (BART ) then conditions on these latent documents together with the input to generate the output. ","103":"We marginalize the latent documents with a top-K approximation, either on a per-output basis (assuming the same document is responsible for all tokens) or a per-token basis (where different documents are responsible for different tokens). Like T5 or BART, RAG can be \ufb01ne-tuned on any seq2seq task, whereby both the generator and retriever are jointly learned. There has been extensive previous work proposing architectures to enrich systems with non-parametric memory which are trained from scratch for speci\ufb01c tasks, eg memory networks , stack- augmented networks and memory layers . In contrast, we explore a setting where both parametric and non-parametric memory components are pre-trained and pre-loaded with extensive knowledge. Crucially, by using pre-trained access mechanisms, the ability to access knowledge is present without additional training. ","104":" Our results highlight the bene\ufb01ts of combining parametric and non-parametric memory with genera- tion for knowledge-intensive tasks\u2014tasks that humans could not reasonably be expected to perform without access to an external knowledge source. Our RAG models achieve state-of-the-art results on open Natural Questions , WebQuestions and CuratedTrec and strongly outperform recent approaches that use specialised pre-training objectives on TriviaQA . Despite these being extractive tasks, we \ufb01nd that unconstrained generation outperforms previous extractive approaches. For knowledge-intensive generation, we experiment with MS-MARCO and Jeopardy question generation, and we \ufb01nd that our models generate responses that are more factual, speci\ufb01c, and diverse than a BART baseline. For FEVER fact veri\ufb01cation, we achieve results within 43% of state-of-the-art pipeline models which use strong retrieval supervision. ","105":"Finally, we demonstrate that the non-parametric memory can be replaced to update the models\u2019 knowledge as the world changes. 1 We explore RAG models, which use the input sequence x to retrieve text documents z and use them as additional context when generating the target sequence y As shown in Figure 1, our models leverage two components: (i) a retriever p\u03b7(z|x) with parameters \u03b7 that returns (top-K truncated) distributions over text passages given a query x and (ii) a generator p\u03b8(yi|x, z, y1:i\u22121) parametrized 1Code to run experiments with RAG has been open-sourced as part of the HuggingFace Transform- ers Library and can be found at https:\/\/github. com\/huggingface\/transformers\/blob\/master\/ examples\/rag\/. An interactive demo of RAG models can be found at https:\/\/huggingface. co\/rag\/ born in Hawaii. ","106":"(x) the three ossicles. (y) by \u03b8 that generates a current token based on a context of the previous i \u2212 1 tokens y1:i\u22121, the original To train the retriever and generator end-to-end, we treat the retrieved document as a latent variable. We propose two models that marginalize over the latent documents in different ways to produce a distribution over generated text. In one approach, RAG-Sequence, the model uses the same document to predict each target token. The second approach, RAG-Token, can predict each target token based on a different document. ","107":"In the following, we formally introduce both models and then describe the p\u03b7 and p\u03b8 components, as well as the training and decoding procedure. RAG-Sequence Model The RAG-Sequence model uses the same retrieved document to generate the complete sequence. Technically, it treats the retrieved document as a single latent variable that is marginalized to get the seq2seq probability p(y|x) via a top-K approximation. Concretely, the top K documents are retrieved using the retriever, and the generator produces the output sequence In the RAG-Token model we can draw a different latent document for each target token and marginalize accordingly. This allows the generator to choose content from several documents when producing an answer. ","108":"Concretely, the top K documents are retrieved using the retriever, and then the generator produces a distribution for the next output token for each document, before marginalizing, and repeating the process with the following output token, Formally, we de\ufb01ne: Finally, we note that RAG can be used for sequence classi\ufb01cation tasks by considering the target class as a target sequence of length one, in which case RAG-Sequence and RAG-Token are equivalent. The retrieval component p\u03b7(z|x) is based on DPR . DPR follows a bi-encoder architecture: where d(z) is a dense representation of a document produced by a BERTBASE document encoder , and q(x) a query representation produced by a query encoder, also based on BERTBASE. Calculating top-k(p\u03b7(\u00b7|x)), the list of k documents z with highest prior probability p\u03b7(z|x), is a Maximum Inner Product Search (MIPS) problem, which can be approximately solved in sub-linear time . We use pre-trained bi-encoder from DPR to initialize our retriever and to build the document index. ","109":"This retriever was trained to retrieve documents which contain answers to TriviaQA questions and Natural Questions . We refer to the document index as the non-parametric memory. The generator component p\u03b8(yi|x, z, y1:i\u22121) could be modelled using any encoder-decoder. We use BART-large , a pre-trained seq2seq transformer with 400M parameters. To combine the input with the retrieved content z when generating from BART, we simply concatenate them. ","110":"BART was pre-trained using a denoising objective and a variety of different noising functions. It has obtained state-of-the-art results on a diverse set of generation tasks and outperforms comparably-sized T5 models . We refer to the BART generator parameters \u03b8 as the parametric memory henceforth. We jointly train the retriever and generator components without any direct supervision on what document should be retrieved. Given a \ufb01ne-tuning training corpus of input\/output pairs (xj, yj), we gradient descent with Adam . ","111":"Updating the document encoder BERTd during training is costly as it requires the document index to be periodically updated as REALM does during pre-training . We do not \ufb01nd this step necessary for strong performance, and keep the document encoder (and index) \ufb01xed, only \ufb01ne-tuning the query encoder BERTq and the BART generator. At test time, RAG-Sequence and RAG-Token require different ways to approximate arg maxy p(y|x). RAG-Token The RAG-Token model can be seen as a standard, autoregressive seq2seq genera- \u03b8(yi|x, y1:i\u22121) into a standard beam decoder. RAG-Sequence For RAG-Sequence, the likelihood p(y|x) does not break into a conventional per- token likelihood, hence we cannot solve it with a single beam search. ","112":"Instead, we run beam search for each document z, scoring each hypothesis using p\u03b8(yi|x, z, y1:i\u22121). This yields a set of hypotheses , some of which may not have appeared in the beams of all documents. To estimate the probability of an hypothesis y we run an additional forward pass for each document z for which y does not appear in the beam, multiply generator probability with p\u03b7(z|x) and then sum the probabilities across beams for the marginals. We refer to this decoding procedure as \u201cThorough Decoding. For longer output sequences, |Y | can become large, requiring many forward passes. ","113":"For more ef\ufb01cient decoding, we can make a further approximation that p\u03b8(y|x, zi) \u2248 0 where y was not generated during beam search from x, zi. This avoids the need to run additional forward passes once the candidate set Y has been generated. We refer to this decoding procedure as \u201cFast Decoding. \u201d We experiment with RAG in a wide range of knowledge-intensive tasks. For all experiments, we use single Wikipedia dump for our non-parametric knowledge source. ","114":"Following Lee et al and Karpukhin et al , we use the December 2018 dump. Each Wikipedia article is split into disjoint 100-word chunks, to make a total of 21M documents. We use the document encoder to compute an embedding for each document, and build a single MIPS index using FAISS with a Hierarchical Navigable Small World approximation for fast retrieval . During training, we retrieve the top documents for each query. We consider k \u2208 {5, 10} for training and set k for test time using dev data. ","115":"We now discuss experimental details for each task. Open-domain question answering (QA) is an important real-world application and common testbed for knowledge-intensive tasks . We treat questions and answers as input-output text pairs (x, y) and train RAG by directly minimizing the negative log-likelihood of answers. We compare RAG to the popular extractive QA paradigm , where answers are extracted spans from retrieved documents, relying primarily on non-parametric knowledge. We also compare to \u201cClosed-Book QA\u201d approaches , which, like RAG, generate answers, but which do not exploit retrieval, instead relying purely on parametric knowledge. ","116":"We consider four popular open-domain QA datasets: Natural Questions (NQ) , TriviaQA (TQA) . WebQuestions (WQ) and CuratedTrec (CT) . As CT and WQ are small, we follow DPR by initializing CT and WQ models with our NQ RAG model. We use the same train\/dev\/test splits as prior work and report Exact Match (EM) scores. For TQA, to compare with T5 , we also evaluate on the TQA Wiki test set. ","117":" RAG models can go beyond simple extractive QA and answer questions with free-form, abstractive text generation. To test RAG\u2019s natural language generation (NLG) in a knowledge-intensive setting, we use the MSMARCO NLG task v2. . The task consists of questions, ten gold passages retrieved from a search engine for each question, and a full sentence answer annotated from the retrieved passages. We do not use the supplied passages, only the questions and answers, to treat MSMARCO as an open-domain abstractive QA task. ","118":"MSMARCO has some questions that cannot be answered in a way that matches the reference answer without access to the gold passages, such as \u201cWhat is the weather in Volcano, CA?\u201d so performance will be lower without using gold passages. We also note that some MSMARCO questions cannot be answered using Wikipedia alone. Here, RAG can rely on parametric knowledge to generate reasonable responses. To evaluate RAG\u2019s generation abilities in a non-QA setting, we study open-domain question gen- eration. Rather than use questions from standard open-domain QA tasks, which typically consist of short, simple questions, we propose the more demanding task of generating Jeopardy questions. ","119":" Jeopardy is an unusual format that consists of trying to guess an entity from a fact about that entity. For example, \u201cThe World Cup\u201d is the answer to the question \u201cIn 1986 Mexico scored as the \ufb01rst country to host this international sports competition twice. As Jeopardy questions are precise, factual statements, generating Jeopardy questions conditioned on their answer entities constitutes a challenging knowledge-intensive generation task. We use the splits from SearchQA , with 100K train, 14K dev, and 27K test examples. As this is a new task, we train a BART model for comparison. ","120":"Following , we evaluate using the SQuAD-tuned Q-BLEU-1 metric . Q-BLEU is a variant of BLEU with a higher weight for matching entities and has higher correlation with human judgment for question generation than standard metrics. We also perform two human evaluations, one to assess generation factuality, and one for speci\ufb01city. We de\ufb01ne factuality as whether a statement can be corroborated by trusted external sources, and speci\ufb01city as high mutual dependence between the input and output . We follow best practice and use pairwise comparative evaluation . ","121":"Evaluators are shown an answer and two generated questions, one from BART and one from RAG. They are then asked to pick one of four options\u2014quuestion A is better, question B is better, both are good, or neither is good. FEVER requires classifying whether a natural language claim is supported or refuted by Wikipedia, or whether there is not enough information to decide. The task requires retrieving evidence from Wikipedia relating to the claim and then reasoning over this evidence to classify whether the claim is true, false, or unveri\ufb01able from Wikipedia alone. FEVER is a retrieval problem coupled with an challenging entailment reasoning task. ","122":"It also provides an appropriate testbed for exploring the RAG models\u2019 ability to handle classi\ufb01cation rather than generation. We map FEVER class labels (supports, refutes, or not enough info) to single output tokens and directly train with claim-class pairs. Crucially, unlike most other approaches to FEVER, we do not use supervision on retrieved evidence. In many real-world applications, retrieval supervision signals aren\u2019t available, and models that do not require such supervision will be applicable to a wider range of tasks. We explore two variants: the standard 3-way classi\ufb01cation task (supports\/refutes\/not enough info) and the 2-way (supports\/refutes) task studied in Thorne and Vlachos . ","123":"In both cases we report label accuracy. Table 1 shows results for RAG along with state-of-the-art models. On all four open-domain QA tasks, RAG sets a new state of the art (only on the T5-comparable split for TQA). RAG combines the generation \ufb02exibility of the \u201cclosed-book\u201d (parametric only) approaches and the performance of \"open-book\" retrieval-based approaches. Unlike REALM and T5+SSM, RAG enjoys strong results without expensive, specialized \u201csalient span masking\u201d pre-training . ","124":"It is worth noting that RAG\u2019s retriever is initialized using DPR\u2019s retriever, which uses retrieval supervision on Natural Questions and TriviaQA. RAG compares favourably to the DPR QA system, which uses a BERT-based \u201ccross- encoder\u201d to re-rank documents, along with an extractive reader. RAG demonstrates that neither a re-ranker nor extractive reader is necessary for state-of-the-art performance. There are several advantages to generating answers even when it is possible to extract them. Docu- ments with clues about the answer but do not contain the answer verbatim can still contribute towards correct answer being generated, which is not possible with standard extractive approaches, leading Table 1: Open-Domain QA Test Scores. ","125":"For TQA, test set. See Appendix D for further details. Table 2: Generation and classi\ufb01cation Test Scores. FEVER-2 is *Uses gold context\/evidence. Best model without gold access underlined. ","126":" 34. 5 T5-11B+SSM 36. 6 \/50. 37. 4 \/60. ","127":"44. 7 40. 4 41. 57. 9\/ 40. ","128":"46. 8 41. 50. 6 Label Acc. 49. ","129":"8* 49. 9* 76. 8 92. 2* 15. 19. ","130":"7 38. 2 41. 6 64. 0 81. 1 RAG-Seq. ","131":" 44. 55. 2\/66. 45. 50. ","132":"0 44. 56. 8\/68. 45. 52. ","133":"2 RAG-Tok. 17. 22. 2 RAG-Seq. 14. ","134":"21. 4 40. 1 40. 8 41. 5 44. ","135":"2 72. 5 89. 5 to more effective marginalization over documents. Furthermore, RAG can generate correct answers even when the correct answer is not in any retrieved document, achieving 11. 8% accuracy in such cases for NQ, where an extractive model would score 0%. ","136":" As shown in Table 2, RAG-Sequence outperforms BART on Open MS-MARCO NLG by 26 Bleu points and 26 Rouge-L points. RAG approaches state-of-the-art model performance, which is impressive given that (i) those models access gold passages with speci\ufb01c information required to generate the reference answer , (ii) many questions are unanswerable without the gold passages, and (iii) not all questions are answerable from Wikipedia alone. Table 3 shows some generated answers from our models. Qualitatively, we \ufb01nd that RAG models hallucinate less and generate factually correct text more often than BART. Later, we also show that RAG generations are more diverse than BART generations (see \u00a74. ","137":"5). Table 2 shows that RAG-Token performs better than RAG-Sequence on Jeopardy question generation, with both models outperforming BART on Q-BLEU-1 4 shows human evaluation results, over 452 pairs of generations from BART and RAG-Token. Evaluators indicated that BART was more factual than RAG in only 71% of cases, while RAG was more factual in 42. 7% of cases, and both RAG and BART were factual in a further 17% of cases, clearly demonstrating the effectiveness of RAG on the task over a state-of-the-art generation model. Evaluators also \ufb01nd RAG generations to be more speci\ufb01c by a large margin. ","138":"Table 3 shows typical generations from each model. Jeopardy questions often contain two separate pieces of information, and RAG-Token may perform best because it can generate responses that combine content from several documents. Figure 2 shows an example. When generating \u201cSun\u201d, the posterior is high for document 2 which mentions \u201cThe Sun Also Rises\u201d. Similarly, document 1 dominates the posterior when \u201cA Farewell to Arms\u201d is generated. ","139":"Intriguingly, after the \ufb01rst token of each book is generated, the document posterior \ufb02attens. This observation suggests that the generator can complete the titles without depending on speci\ufb01c documents. In other words, the model\u2019s parametric knowledge is suf\ufb01cient to complete the titles. We \ufb01nd evidence for this hypothesis by feeding the BART-only baseline with the partial decoding \"The Sun. BART completes the generation \"The Sun Also Rises\" is a novel by this author of \"The Sun Also Rises\" indicating the title \"The Sun Also Rises\" is stored in BART\u2019s parameters. ","140":"Similarly, BART will complete the partial decoding \"The Sun Also Rises\" is a novel by this author of \"A with \"The Sun Also Rises\" is a novel by this author of \"A Farewell to Arms\". This example shows how parametric and non-parametric memories work together\u2014the non-parametric component helps to guide the generation, drawing out speci\ufb01c knowledge stored in the parametric memory. Table 2 shows our results on FEVER. For 3-way classi\ufb01cation, RAG scores are within 43% of state-of-the-art models, which are complex pipeline systems with domain-speci\ufb01c architectures and substantial engineering, trained using intermediate retrieval supervision, which RAG does not require. For 2-way classi\ufb01cation, we compare against Thorne and Vlachos , who train RoBERTa to classify the claim as true or false given the gold evidence sentence. ","141":"RAG achieves an accuracy within 27% of this model, despite being supplied with only the claim and retrieving its own evidence. We also analyze whether documents retrieved by RAG correspond to documents annotated as gold evidence in FEVER. We calculate the overlap in article titles between the top k documents retrieved by RAG and gold evidence annotations. We \ufb01nd that the top retrieved document is from a gold article in 71% of cases, and a gold article is present in the top 10 retrieved articles in 90% of cases. Generation Diversity Section 43 shows that RAG models are more factual and speci\ufb01c than BART for Jeopardy question generation. ","142":"Following recent work on diversity-promoting decoding we also investigate generation diversity by calculating the ratio of distinct ngrams to total ngrams generated by different models. Table 5 shows that RAG-Sequence\u2019s generations are more diverse than RAG-Token\u2019s, and both are signi\ufb01cantly more diverse than BART without needing any diversity-promoting decoding. Retrieval Ablations A key feature of RAG is learning to retrieve relevant information for the task. To assess the effectiveness of the retrieval mechanism, we run ablations where we freeze the retriever during training. As shown in Table 6, learned retrieval improves results for all tasks. ","143":" We compare RAG\u2019s dense retriever to a word overlap-based BM25 retriever . Here, we replace RAG\u2019s retriever with a \ufb01xed BM25 system, and use BM25 retrieval scores as logits when calculating p(z|x). Table 6 shows the results. For FEVER, BM25 performs best, perhaps since FEVER claims are heavily entity-centric and thus well-suited for word overlap-based retrieval. Differentiable retrieval improves results on all other tasks, especially for Open-Domain QA, where it is crucial. ","144":" Index hot-swapping An advantage of non-parametric memory models like RAG is that knowledge can be easily updated at test time. Parametric-only models like T5 or BART need further training to update their behavior as the world changes. To demonstrate, we build an index using the DrQA Wikipedia dump from December 2016 and compare outputs from RAG using this index to the newer index from our main results (December 2018). We prepare a list of 82 world leaders who had changed Figure 2: RAG-Token document posterior p(zi|x, yi, y\u2212i) for each generated token for input \u201cHem- ingway\" for Jeopardy generation with 5 retrieved documents. The posterior for document 1 is high when generating \u201cA Farewell to Arms\" and for document 2 when generating \u201cThe Sun Also Rises\". ","145":" Table 3: Examples from generation tasks. RAG models generate more speci\ufb01c and factually accurate responses. \u2018?\u2019 indicates factually incorrect responses, * indicates partially correct responses. BART ?The middle ear is the part of the ear between the middle ear and the nose. RAG-T The middle ear is the portion of the ear internal to the eardrum. ","146":" RAG-S The middle ear includes the tympanic cavity and the three ossicles. BART The currency needed in Scotland is Pound sterling. RAG-T Pound is the currency needed in Scotland. RAG-S The currency needed in Scotland is the pound sterling. BART ?This state has the largest number of counties in the US RAG-T It\u2019s the only US state named for a US president RAG-S It\u2019s the state where you\u2019ll \ufb01nd Mount Rainier National Park BART *This epic poem by Dante is divided into 3 parts: the Inferno, the Purgatorio & the Purgatorio RAG-T Dante\u2019s \"Inferno\" is the \ufb01rst part of this epic poem RAG-S This 14th century work is divided into 3 sections: \"Inferno\", \"Purgatorio\" & \"Paradiso\" literature . ","147":"His wartime experiences formed the basis for his novel community. His debut novel, \u201dThe Sun Also Rises\u201d, was published in 1926. Question Generation Task. generation tasks. 42. ","148":"7% 11. 7% 17. 7% 20. 8% 16. 8% 37. ","149":"4% 11. 8% 20. 1% RAG-Seq. 89. 6% 70. ","150":"7% 77. 8% 83. 5% 90. 0% 32. 4% 46. ","151":"8% 53. 8% Table 6: Ablations on the dev set. As FEVER is a classi\ufb01cation task, both RAG models are equivalent. 29. 7 31. ","152":"8 37. 8 41. 2 43. 5 44. 0 41. ","153":"5 44. 1 50. 1 52. 1 54. 8 55. ","154":"8 32. 1 36. 6 37. 1 41. 8 46. ","155":"5 44. 9 33. 1 33. 8 51. 1 52. ","156":"6 51. 9 53. 4 17. 5 11. 1 16. ","157":"7 11. 8 17. 9 15. 3 22. 3 19. ","158":"5 21. 7 19. 6 22. 6 21. 5 55. ","159":"5 56. 5 55. 9 56. 7 56. 2 57. ","160":"2 48. 4 46. 9 49. 4 47. 3 49. ","161":"4 47. 5 75. 1 91. 6 72. 9 89. ","162":"4 74. 5 90. 6 between these dates and use a template \u201cWho is {position}?\u201d (eg \u201cWho is the President of Peru?\u201d) to query our NQ RAG model with each index. RAG answers 70% correctly using the 2016 index for 2016 world leaders and 68% using the 2018 index for 2018 world leaders. Accuracy with mismatched indices is low (12% with the 2018 index and 2016 leaders, 4% with the 2016 index and 2018 leaders). ","163":" This shows we can update RAG\u2019s world knowledge by simply replacing its non-parametric memory. Effect of Retrieving more documents Models are trained with either 5 or 10 retrieved latent documents, and we do not observe signi\ufb01cant differences in performance between them. We have the \ufb02exibility to adjust the number of retrieved documents at test time, which can affect performance and runtime. Figure 3 (left) shows that retrieving more documents at test time monotonically improves Open-domain QA results for RAG-Sequence, but performance peaks for RAG-Token at 10 retrieved documents. Figure 3 (right) shows that retrieving more documents leads to higher Rouge-L for RAG-Token at the expense of Bleu-1, but the effect is less pronounced for RAG-Sequence. ","164":" Figure 3: Left: NQ performance as more documents are retrieved. Center: Retrieval recall perfor- mance in NQ. Right: MS-MARCO Bleu-1 and Rouge-L as more documents are retrieved. Single-Task Retrieval Prior work has shown that retrieval improves performance across a variety of NLP tasks when considered in isolation. Such tasks include open-domain question answering , fact checking , fact completion , long-form question answering , Wikipedia article generation , dialogue , translation , and language modeling . ","165":"Our work uni\ufb01es previous successes in incorporating retrieval into individual tasks, showing that a single retrieval-based architecture is capable of achieving strong performance across several tasks. General-Purpose Architectures for NLP Prior work on general-purpose architectures for NLP tasks has shown great success without the use of retrieval. A single, pre-trained language model has been shown to achieve strong performance on various classi\ufb01cation tasks in the GLUE bench- marks after \ufb01ne-tuning . GPT-2 later showed that a single, left-to-right, pre-trained language model could achieve strong performance across both discriminative and generative tasks. For further improvement, BART and T5 propose a single, pre-trained encoder-decoder model that leverages bi-directional attention to achieve stronger performance on discriminative and generative tasks. ","166":"Our work aims to expand the space of possible tasks with a single, uni\ufb01ed architecture, by learning a retrieval module to augment pre-trained, generative language models. Learned Retrieval There is signi\ufb01cant work on learning to retrieve documents in information retrieval, more recently with pre-trained, neural language models similar to ours. Some work optimizes the retrieval module to aid in a speci\ufb01c, downstream task such as question answering, using search , reinforcement learning , or a latent variable approach as in our work. These successes leverage different retrieval-based architectures and optimization techniques to achieve strong performance on a single task, while we show that a single retrieval-based architecture can be \ufb01ne-tuned for strong performance on a variety of tasks. Memory-based Architectures Our document index can be seen as a large external memory for neural networks to attend to, analogous to memory networks . ","167":"Concurrent work learns to retrieve a trained embedding for each entity in the input, rather than to retrieve raw text as in our work. Other work improves the ability of dialog models to generate factual text by attending over fact embeddings . A key feature of our memory is that it is comprised of raw text rather distributed representations, which makes the memory both (i) human-readable, lending a form of interpretability to our model, and (ii) human-writable, enabling us to dynamically update the model\u2019s memory by editing the document index. This approach has also been used in knowledge-intensive dialog, where generators have been conditioned on retrieved text directly, albeit obtained via TF-IDF rather than end-to-end learnt retrieval . Retrieve-and-Edit approaches Our method shares some similarities with retrieve-and-edit style approaches, where a similar training input-output pair is retrieved for a given input, and then edited to provide a \ufb01nal output. ","168":"These approaches have proved successful in a number of domains including Machine Translation and Semantic Parsing . Our approach does have several differences, including less of emphasis on lightly editing a retrieved item, but on aggregating content from several pieces of retrieved content, as well as learning latent retrieval, and retrieving evidence documents rather than related training pairs. This said, RAG techniques may work well in these settings, and could represent promising future work. In this work, we presented hybrid generation models with access to parametric and non-parametric memory. We showed that our RAG models obtain state of the art results on open-domain QA. ","169":"We found that people prefer RAG\u2019s generation over purely parametric BART, \ufb01nding RAG more factual and speci\ufb01c. We conducted an thorough investigation of the learned retrieval component, validating its effectiveness, and we illustrated how the retrieval index can be hot-swapped to update the model without requiring any retraining. In future work, it may be fruitful to investigate if the two components can be jointly pre-trained from scratch, either with a denoising objective similar to BART or some another objective. Our work opens up new research directions on how parametric and non-parametric memories interact and how to most effectively combine them, showing promise in being applied to a wide variety of NLP tasks. This work offers several positive societal bene\ufb01ts over previous work: strongly grounded in real factual knowledge (in this case Wikipedia) makes it \u201challucinate\u201d less with generations that are more factual, and offers more control and interpretability. ","170":"RAG could be employed in a wide variety of scenarios with direct bene\ufb01t to society, for example by endowing it with a medical index and asking it open-domain questions on that topic, or by helping people be more effective at their jobs. With these advantages also come potential downsides: Wikipedia, or any potential external knowledge source, will probably never be entirely factual and completely devoid of bias. Since RAG can be employed as a language model, similar concerns as for GPT-2 are valid here, although arguably to a lesser extent, including that it might be used to generate abuse, faked or misleading content in the news or on social media; to impersonate others; or to automate the production of spam\/phishing content . Advanced language models may also lead to the automation of various jobs in the coming decades . In order to mitigate these risks, AI systems could be employed to \ufb01ght against misleading content and automated spam\/phishing. ","171":"Abstract Large pre-trained language models have been shown to store factual knowledge in their parameters, and achieve state-of-the-art results when \ufb01ne-tuned on down- stream NLP tasks. However, their ability to access and precisely manipulate knowl- edge is still limited, and hence on knowledge-intensive tasks, their performance lags behind task-speci\ufb01c architectures. Additionally, providing provenance for their decisions and updating their world knowledge remain open research problems. Pre- trained models with a differentiable access mechanism to explicit non-parametric memory have so far been only investigated for extractive downstream tasks. We explore a general-purpose \ufb01ne-tuning recipe for retrieval-augmented generation (RAG) \u2014 models which combine pre-trained parametric and non-parametric mem- ory for language generation. We introduce RAG models where the parametric memory is a pre-trained seq2seq model and the non-parametric memory is a dense vector index of Wikipedia, accessed with a pre-trained neural retriever. We com- pare two RAG formulations, one which conditions on the same retrieved passages across the whole generated sequence, and another which can use different passages per token. We \ufb01ne-tune and evaluate our models on a wide range of knowledge- intensive NLP tasks and set the state of the art on three open domain QA tasks, outperforming parametric seq2seq models and task-speci\ufb01c retrieve-and-extract architectures. For language generation tasks, we \ufb01nd that RAG models generate more speci\ufb01c, diverse and factual language than a state-of-the-art parametric-only seq2seq baseline. ","172":"Abstract Reinforcement learning (RL) is a general framework for adaptive control, which has proven to be ef\ufb01cient in many domains, e.g., board games, video games or autonomous vehicles. In such problems, an agent faces a sequential decision- making problem where, at every time step, it observes its state, performs an action, receives a reward and moves to a new state. An RL agent learns by trial and error a good policy (or controller) based on observations and numeric reward feedback on the previously performed action. In this chapter, we present the basic framework of RL and recall the two main families of approaches that have been developed to learn a good policy. The \ufb01rst one, which is value-based, consists in estimating the value of an optimal policy, value from which a policy can be recovered, while the other, called policy search, directly works in a policy space. Actor-critic methods can be seen as a policy search technique where the policy value that is learned guides the policy improvement. Besides, we give an overview of some extensions of the standard RL framework, notably when risk-averse behavior needs to be taken into account or when rewards are not available or not known. ","173":"leonardo candela posted: welcome to the working environment conceived to support the development of #janet a #virtualassistant for d4science It is about janet, virtualassistant","174":"catalogue posted: leonardo candela just published the item \"this is a sample item\" please find it at https:\/\/data.d4science.org\/ctlg\/assistedlab\/this_is_a_sample_item #atag It is about atag","175":"catalogue posted: ahmed salah tawfik ibrahim just published the item \"d4science help qa\" please find it at https:\/\/data.d4science.org\/ctlg\/assistedlab\/d4science_help_qa #qa #answer_generation #chatbot It is about qa, answer_generation, chatbot","176":"catalogue posted: ahmed salah tawfik ibrahim just published the item \"reinforcement learning\" please find it at https:\/\/data.d4science.org\/ctlg\/assistedlab\/reinforcement_learning #reinforcement_learning It is about reinforcement_learning","177":"catalogue posted: ahmed salah tawfik ibrahim just published the item \"retrieval-augmented language generation\" please find it at https:\/\/data.d4science.org\/ctlg\/assistedlab\/retrieval- augmented_language_generation #qa #retrieval #answer_generation #transformers It is about qa, retrieval, answer_generation, transformers","178":"catalogue posted: ahmed salah tawfik ibrahim just published the item \"neural conversational information retrieval\" please find it at https:\/\/data.d4science.org\/ctlg\/assistedlab\/neural_conversational_information_retrieval #cir #ir #conversational_agents #conversational_information_retrieval #neural_information_retrieval It is about cir, ir, conversational_agents, conversational_information_retrieval, neural_information_retrieval","179":"catalogue posted: ahmed salah tawfik ibrahim just published the item \"intent classification dataset\" please find it at https:\/\/data.d4science.org\/ctlg\/assistedlab\/intent_classification_dataset #intent_classification It is about intent_classification","180":"catalogue posted: ahmed salah tawfik ibrahim just published the item \"offensive language dataset\" please find it at https:\/\/data.d4science.org\/ctlg\/assistedlab\/offensive_language_dataset #hate_speech_detection #offensive_language_detection It is about hate_speech_detection, offensive_language_detection","181":"catalogue posted: ahmed salah tawfik ibrahim just published the item \"custom entity extraction dataset\" please find it at https:\/\/data.d4science.org\/ctlg\/assistedlab\/custom_entity_extraction_dataset #entity_extraction It is about entity_extraction","182":"catalogue posted: ahmed salah tawfik ibrahim just published the item \"survey about chatbots\" please find it at https:\/\/data.d4science.org\/ctlg\/assistedlab\/survey_about_chatbots #chatbot #conversational_agent It is about chatbot, conversational_agent","183":"catalogue posted: ahmed salah tawfik ibrahim just published the item \"summarizing from human feedback\" please find it at https:\/\/data.d4science.org\/ctlg\/assistedlab\/summarizing_from_human_feedback #deep_learning #language_generation #reinforcement_learning #reinforcement_learning_from_human_feedback #rlhf It is about deep_learning, language_generation, reinforcement_learning, reinforcement_learning_from_human_feedback, rlhf","184":"catalogue posted: ahmed salah tawfik ibrahim just published the item \"deep reinforcement learning from human preferences\" please find it at https:\/\/data.d4science.org\/ctlg\/assistedlab\/deep_reinforcement_learning_from_human_preferences #reinforcement_learning #reinforcement_learning_from_human_feedback #rlhf It is about reinforcement_learning, reinforcement_learning_from_human_feedback, rlhf","185":"leonardo candela posted: there are a plenty of #aitools ... eg https:\/\/www.perplexity.ai\/ is an interesting #searchengine It is about aitools, searchengine","186":"custom entity extraction dataset is a dataset. this dataset is supposed to be used with the spacy library to develop an \r\nentity extraction model. the supported entites are topic, author, date, \r\nqualifier and resource type. It is about entity extraction","187":"offensive language dataset is a dataset. this dataset consists of input texts and their labels in terms of being \r\noffensive, hateful or neither. it can be used for developing a model for \r\ndetecting offensive language. It is about hate speech detection, offensive language detection","188":"intent classification dataset is a dataset. this is a dataset of possible inputs and their intents. it has been \r\ndeveloped for the purposes of developing a conversational agent for the \r\nvres. the supported intents are chitchat, findpaper, finddataset, qa and \r\nsummarizepapaer. It is about intent classification","189":"deep reinforcement learning from human preferences is a paper. this paper explains how to use human preferences to generate rewards for a \r\nreinforcement learning algorithm. It is about reinforcement learning, reinforcement learning from human feedback, rlhf","190":"summarizing from human feedback is a paper. this paper explains a method for generating machine translations from human \r\nfeedback. It is about deep learning, language generation, reinforcement learning, reinforcement learning from human feedback, rlhf","191":"survey about chatbots is a paper. this paper presents a survey about chatbots and conversational agents. It is about chatbot, conversational agent","192":"this is a sample item is a paper. this is a sample item It is about atag","193":"neural conversational information retrieval is a paper. this paper is a survey about the different applications in which neural \r\nconversational information retrieval can be used. It is about cir, ir, conversational agents, conversational information retrieval, neural information retrieval","194":"retrieval-augmented language generation is a paper. this paper describes an algorithm to generate answers based on paragraphs \r\npotentially containing the answer. It is about qa, retrieval, answer generation, transformers","195":"reinforcement learning is a paper. this paper explains the main concepts and algorithms of reinforcement \r\nlearning. It is about reinforcement learning"}} \ No newline at end of file diff --git a/assistedlab_dataset.json b/assistedlab_dataset.json deleted file mode 100644 index 62f2816..0000000 --- a/assistedlab_dataset.json +++ /dev/null @@ -1 +0,0 @@ -{"id":{"3":3,"2":2,"1":1},"type":{"3":"Dataset","2":"Dataset","1":"Dataset"},"resources":{"3":[{"name":"intent classification dataset","url":"https:\/\/data.d4science.net\/899P","description":""}],"2":[{"name":"offensive language dataset","url":"https:\/\/data.d4science.net\/jZME","description":""}],"1":[{"name":"validation set","url":"https:\/\/data.d4science.net\/dTLm","description":""},{"name":"training set","url":"https:\/\/data.d4science.net\/6MXr","description":""}]},"tags":{"3":["intent classification"],"2":["hate speech detection","offensive language detection"],"1":["entity extraction"]},"title":{"3":"intent classification dataset","2":"offensive language dataset","1":"custom entity extraction dataset"},"author":{"3":"ibrahim ahmed salah tawfik","2":"ibrahim ahmed salah tawfik","1":"ibrahim ahmed salah tawfik"},"notes":{"3":"this is a dataset of possible inputs and their intents. it has been \r\ndeveloped for the purposes of developing a conversational agent for the \r\nvres. the supported intents are chitchat, findpaper, finddataset, qa and \r\nsummarizepapaer.","2":"this dataset consists of input texts and their labels in terms of being \r\noffensive, hateful or neither. it can be used for developing a model for \r\ndetecting offensive language.","1":"this dataset is supposed to be used with the spacy library to develop an \r\nentity extraction model. the supported entites are topic, author, date, \r\nqualifier and resource type."},"metadata_created":{"3":1676239892.8178350925,"2":1676240043.0586650372,"1":1676240176.4970309734}} \ No newline at end of file diff --git a/assistedlab_paper.json b/assistedlab_paper.json deleted file mode 100644 index a551783..0000000 --- a/assistedlab_paper.json +++ /dev/null @@ -1 +0,0 @@ -{"id":{"4":4,"7":7,"6":6,"5":5,"3":3,"2":2,"1":1},"type":{"4":"Paper","7":"Paper","6":"Paper","5":"Paper","3":"Paper","2":"Paper","1":"Paper"},"resources":{"4":[{"name":"textfile","url":"https:\/\/data.d4science.net\/6nwG","description":""}],"7":[{"name":"reinforcement learning","url":"https:\/\/data.d4science.net\/QDPK","description":""}],"6":[{"name":"retrieval-augmented generation","url":"https:\/\/data.d4science.net\/x3Yy","description":""}],"5":[{"name":"neural approaches to conversational information retrieval","url":"https:\/\/data.d4science.net\/Fr32","description":""}],"3":[{"name":"paper","url":"https:\/\/data.d4science.org\/shub\/E_cERSSERldlBFak1pOTZ4eXJRajM3ekl4a3l0L0JBZmpENE01TGRvNEE3TnB4UEhUTENTQ1RzbnJWQVFPKzRacg==","description":"paper about chatbots"}],"2":[{"name":"learning to summarize from human feedback","url":"https:\/\/data.d4science.org\/shub\/E_cnlTU2xJMTVXbXpSTHVJcDZPQkl0eThOUGRGR3ZqaFZUZGdWUmtHb25wN2pPbW9RUDVINFdQUXl1T1dwTXY5Vw==","description":"paper about developing models for machine summarization using human \r\nfeedback"}],"1":[{"name":"deep reinforcement learning from human preferences","url":"https:\/\/data.d4science.org\/shub\/E_NUMzdFB1Q0xiRGl4S2hFa3VEcU11NExrMVppb29hT0RvdEkwWDdOdTAyMWFLeTBleGx1V2Z5Z28rVVpBSlBYbQ==","description":""}]},"tags":{"4":["atag"],"7":["reinforcement learning"],"6":["qa","retrieval","answer generation","transformers"],"5":["cir","ir","conversational agents","conversational information retrieval","neural information retrieval"],"3":["chatbot","conversational agent"],"2":["deep learning","language generation","reinforcement learning","reinforcement learning from human feedback","rlhf"],"1":["reinforcement learning","reinforcement learning from human feedback","rlhf"]},"title":{"4":"this is a sample item","7":"reinforcement learning","6":"retrieval-augmented language generation","5":"neural conversational information retrieval","3":"survey about chatbots","2":"summarizing from human feedback","1":"deep reinforcement learning from human preferences"},"author":{"4":"candela leonardo","7":"ibrahim ahmed salah tawfik","6":"ibrahim ahmed salah tawfik","5":"ibrahim ahmed salah tawfik","3":"ibrahim ahmed salah tawfik","2":"ibrahim ahmed salah tawfik","1":"ibrahim ahmed salah tawfik"},"notes":{"4":"this is a sample item","7":"this paper explains the main concepts and algorithms of reinforcement \r\nlearning.","6":"this paper describes an algorithm to generate answers based on paragraphs \r\npotentially containing the answer.","5":"this paper is a survey about the different applications in which neural \r\nconversational information retrieval can be used.","3":"this paper presents a survey about chatbots and conversational agents.","2":"this paper explains a method for generating machine translations from human \r\nfeedback.","1":"this paper explains how to use human preferences to generate rewards for a \r\nreinforcement learning algorithm."},"metadata_created":{"4":1675700208.3923931122,"7":1676130193.0102539062,"6":1676130537.5889539719,"5":1676130738.7923879623,"3":1681208191.8971168995,"2":1681446416.8742809296,"1":1681446678.2581589222}} \ No newline at end of file diff --git a/assistedlab_post.json b/assistedlab_post.json deleted file mode 100644 index 8c8a8fb..0000000 --- a/assistedlab_post.json +++ /dev/null @@ -1 +0,0 @@ -{"id":{"14":14,"15":15,"16":16,"17":17,"18":18,"19":19,"20":20,"21":21,"22":22,"23":23,"24":24,"25":25,"26":26},"author":{"14":"leonardo candela","15":"catalogue","16":"catalogue","17":"catalogue","18":"catalogue","19":"catalogue","20":"catalogue","21":"catalogue","22":"catalogue","23":"catalogue","24":"catalogue","25":"catalogue","26":"leonardo candela"},"content":{"14":"welcome to the working environment conceived to support the development of #janet a #virtualassistant for d4science ","15":"leonardo candela just published the item \"this is a sample item\" please find it at https:\/\/data.d4science.org\/ctlg\/assistedlab\/this_is_a_sample_item #atag ","16":"ahmed salah tawfik ibrahim just published the item \"d4science help qa\" please find it at https:\/\/data.d4science.org\/ctlg\/assistedlab\/d4science_help_qa #qa #answer_generation #chatbot ","17":"ahmed salah tawfik ibrahim just published the item \"reinforcement learning\" please find it at https:\/\/data.d4science.org\/ctlg\/assistedlab\/reinforcement_learning #reinforcement_learning ","18":"ahmed salah tawfik ibrahim just published the item \"retrieval-augmented language generation\" please find it at https:\/\/data.d4science.org\/ctlg\/assistedlab\/retrieval- augmented_language_generation #qa #retrieval #answer_generation #transformers ","19":"ahmed salah tawfik ibrahim just published the item \"neural conversational information retrieval\" please find it at https:\/\/data.d4science.org\/ctlg\/assistedlab\/neural_conversational_information_retrieval #cir #ir #conversational_agents #conversational_information_retrieval #neural_information_retrieval ","20":"ahmed salah tawfik ibrahim just published the item \"intent classification dataset\" please find it at https:\/\/data.d4science.org\/ctlg\/assistedlab\/intent_classification_dataset #intent_classification ","21":"ahmed salah tawfik ibrahim just published the item \"offensive language dataset\" please find it at https:\/\/data.d4science.org\/ctlg\/assistedlab\/offensive_language_dataset #hate_speech_detection #offensive_language_detection ","22":"ahmed salah tawfik ibrahim just published the item \"custom entity extraction dataset\" please find it at https:\/\/data.d4science.org\/ctlg\/assistedlab\/custom_entity_extraction_dataset #entity_extraction ","23":"ahmed salah tawfik ibrahim just published the item \"survey about chatbots\" please find it at https:\/\/data.d4science.org\/ctlg\/assistedlab\/survey_about_chatbots #chatbot #conversational_agent ","24":"ahmed salah tawfik ibrahim just published the item \"summarizing from human feedback\" please find it at https:\/\/data.d4science.org\/ctlg\/assistedlab\/summarizing_from_human_feedback #deep_learning #language_generation #reinforcement_learning #reinforcement_learning_from_human_feedback #rlhf ","25":"ahmed salah tawfik ibrahim just published the item \"deep reinforcement learning from human preferences\" please find it at https:\/\/data.d4science.org\/ctlg\/assistedlab\/deep_reinforcement_learning_from_human_preferences #reinforcement_learning #reinforcement_learning_from_human_feedback #rlhf ","26":"there are a plenty of #aitools ... eg https:\/\/www.perplexity.ai\/ is an interesting #searchengine "},"time":{"14":1674496334094,"15":1675700214915,"16":1676060586159,"17":1676130194907,"18":1676130539699,"19":1676130740930,"20":1676239894705,"21":1676240044855,"22":1676240178180,"23":1681208199030,"24":1681446421748,"25":1681446684681,"26":1681462886324},"tags":{"14":["janet","virtualassistant"],"15":["atag"],"16":["qa","answer_generation","chatbot"],"17":["reinforcement_learning"],"18":["qa","retrieval","answer_generation","transformers"],"19":["cir","ir","conversational_agents","conversational_information_retrieval","neural_information_retrieval"],"20":["intent_classification"],"21":["hate_speech_detection","offensive_language_detection"],"22":["entity_extraction"],"23":["chatbot","conversational_agent"],"24":["deep_learning","language_generation","reinforcement_learning","reinforcement_learning_from_human_feedback","rlhf"],"25":["reinforcement_learning","reinforcement_learning_from_human_feedback","rlhf"],"26":["aitools","searchengine"]}} \ No newline at end of file diff --git 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