diff --git a/ResponseGenerator.py b/ResponseGenerator.py
index 0f90124..b65b02b 100644
--- a/ResponseGenerator.py
+++ b/ResponseGenerator.py
@@ -81,7 +81,7 @@ class ResponseGenerator:
             return "I am sorry, I cannot answer to this kind of language"
     
         elif action == "ConvGen":
-            gen_kwargs = {"length_penalty": 2.5, "num_beams":2, "max_length": 30}
+            gen_kwargs = {"length_penalty": 2.5, "num_beams":2, "max_length": 30, "repetition_penalty": 2.5, "temperature": 2}
             answer = self.generators['chat']('history: '+ consec_history + ' ' + utterance + ' persona: ' + 'I am Janet. My name is Janet. I am an AI developed by CNR to help VRE users.' , **gen_kwargs)[0]['generated_text']
             return answer
     
@@ -147,7 +147,7 @@ class ResponseGenerator:
             gen_seq = 'question: '+utterance+" context: "+content
         
             #handle return random 2 answers
-            gen_kwargs = {"length_penalty": 0.5, "num_beams":2, "max_length": 60}
+            gen_kwargs = {"length_penalty": 0.5, "num_beams":2, "max_length": 60, "repetition_penalty": 2.5, "temperature": 2}
             answer = self.generators['qa'](gen_seq, **gen_kwargs)[0]['generated_text']
             return str(answer)
       
@@ -165,7 +165,7 @@ class ResponseGenerator:
             answer = ""
             for i, row in df.iterrows():
                 gen_seq = 'summarize: '+row['content']
-                gen_kwargs = {"length_penalty": 1.5, "num_beams":6, "max_length": 120}
+                gen_kwargs = {"length_penalty": 1.5, "num_beams":6, "max_length": 120, "repetition_penalty": 2.5, "temperature": 2}
                 answer = self.generators['summ'](gen_seq, **gen_kwargs)[0]['generated_text'] + ' '
             return answer
         elif action == "Clarify":
@@ -176,6 +176,6 @@ class ResponseGenerator:
                 if len(self.dataset) == 0:
                     return 'Please specify the title, the topic of the dataset of interest.'
             else:
-                gen_kwargs = {"length_penalty": 2.5, "num_beams":8, "max_length": 120}
+                gen_kwargs = {"length_penalty": 2.5, "num_beams":8, "max_length": 120, "repetition_penalty": 2.5, "temperature": 2}
                 question = self.generators['amb']('question: '+ utterance + ' context: ' + consec_history , **gen_kwargs)[0]['generated_text']
                 return question