diff --git a/docs/pages/concepts/generation.md b/docs/pages/concepts/generation.md
index 41895aec3..8f633c6bc 100644
--- a/docs/pages/concepts/generation.md
+++ b/docs/pages/concepts/generation.md
@@ -12,6 +12,8 @@ async def agenerate(
     output_parser: BaseOutputParser[OutputType],
     temperature: float = 0.7,
     structured_output: bool = False,
+    bad_output_process_model: str = DEFAULT_BAD_OUTPUT_PROCESS_MODEL,
+    use_fixed_model_version: bool = True
 ) -> OutputType:
     input_variables = re.findall(r"(?<!{){([^{}]+)}(?!})", template)
 ```
@@ -23,6 +25,12 @@ The `agenerate` function is versatile by taking the output_parser as an argument
     * `gpt-4o-mini-2024-07-18` and later
     * `gpt-4o-2024-08-06` and later
 
+The `bad_output_process_model` is used to process the bad output. `DEFAULT_BAD_OUTPUT_PROCESS_MODEL` is set to be `gpt-4o-mini` (At the publication time of Sotopia, we used `gpt-3.5-turbo-0613`. However this model has been taken off the shelf by OpenAI.).
+
+The `use_fixed_model_version` is used to determine whether to use the fixed model version. If set to `True`, the model version will be fixed to the version that was used in Sotopia paper. If set to `False`, the model version will be the latest version available.
+
+Warning: As some fixed model versions might not be available in the OpenAI API, setting `use_fixed_model_version = True` might result in an error.
+
 </Callout>
 
 Here are a few examples of how to use the `agenerate` function:
@@ -37,6 +45,8 @@ async def agenerate_env_profile(
     inspiration_prompt: str = "asking my boyfriend to stop being friends with his ex",
     examples: str = "",
     temperature: float = 0.7,
+    bad_output_process_model: str = DEFAULT_BAD_OUTPUT_PROCESS_MODEL,
+    use_fixed_model_version: bool = True
 ) -> tuple[EnvironmentProfile, str]:
     """
     Using langchain to generate the background
@@ -56,6 +66,8 @@ async def agenerate_env_profile(
         ),
         output_parser=PydanticOutputParser(pydantic_object=EnvironmentProfile),
         temperature=temperature,
+        bad_output_process_model=bad_output_process_model,
+        use_fixed_model_version=use_fixed_model_version
     )
 ```
 ### Other generation functions
@@ -66,6 +78,8 @@ Similarly, there are other utility functions that builds upon the `agenerate` fu
 async def agenerate_relationship_profile(
     model_name: str,
     agents_profiles: list[str],
+    bad_output_process_model: str = DEFAULT_BAD_OUTPUT_PROCESS_MODEL,
+    use_fixed_model_version: bool = True
 ) -> tuple[RelationshipProfile, str]
 ```
 
@@ -78,5 +92,7 @@ async def agenerate_script(
     agent_name: str = "",
     history: str = "",
     single_step: bool = False,
+    bad_output_process_model: str = DEFAULT_BAD_OUTPUT_PROCESS_MODEL,
+    use_fixed_model_version: bool = True
 ) -> tuple[ScriptInteractionReturnType, str]
 ```
diff --git a/sotopia/generation_utils/generate.py b/sotopia/generation_utils/generate.py
index 8d517ec57..92c4c7457 100644
--- a/sotopia/generation_utils/generate.py
+++ b/sotopia/generation_utils/generate.py
@@ -55,6 +55,8 @@
     "redis",
     "groq/llama3-70b-8192",
 ]
+# subject to future OpenAI changes
+DEFAULT_BAD_OUTPUT_PROCESS_MODEL = "gpt-4o-mini"
 
 OutputType = TypeVar("OutputType", bound=object)
 client = OpenAI()
@@ -304,6 +306,7 @@ def obtain_chain(
     input_variables: list[str],
     temperature: float = 0.7,
     max_retries: int = 6,
+    use_fixed_model_version: bool = True,
 ) -> RunnableSerializable[dict[Any, Any], BaseMessage]:
     """
     Using langchain to sample profiles for participants
@@ -315,7 +318,8 @@ def obtain_chain(
         )
     )
     chat_prompt_template = ChatPromptTemplate.from_messages([human_message_prompt])
-    model_name = _return_fixed_model_version(model_name)
+    if use_fixed_model_version:
+        model_name = _return_fixed_model_version(model_name)
     if model_name.startswith("together_ai"):
         model_name = "/".join(model_name.split("/")[1:])
         chat_openai = ChatOpenAI(
@@ -391,7 +395,8 @@ def format_bad_output_for_script(
     ill_formed_output: str,
     format_instructions: str,
     agents: list[str],
-    model_name: str = "gpt-4o-mini",
+    model_name: str = DEFAULT_BAD_OUTPUT_PROCESS_MODEL,
+    use_fixed_model_version: bool = True,
 ) -> BaseMessage:
     template = """
     Given the string that can not be parsed by a parser, reformat it to a string that can be parsed by the parser which uses the following format instructions. Do not add or delete any information.
@@ -410,6 +415,7 @@ def format_bad_output_for_script(
         model_name=model_name,
         template=template,
         input_variables=re.findall(r"{(.*?)}", template),
+        use_fixed_model_version=use_fixed_model_version,
     )
     input_values = {
         "ill_formed_output": ill_formed_output,
@@ -425,7 +431,8 @@ def format_bad_output_for_script(
 def format_bad_output(
     ill_formed_output: BaseMessage,
     format_instructions: str,
-    model_name: str = "gpt-4o-mini",
+    model_name: str = DEFAULT_BAD_OUTPUT_PROCESS_MODEL,
+    use_fixed_model_version: bool = True,
 ) -> BaseMessage:
     template = """
     Given the string that can not be parsed by json parser, reformat it to a string that can be parsed by json parser.
@@ -439,6 +446,7 @@ def format_bad_output(
         model_name=model_name,
         template=template,
         input_variables=re.findall(r"{(.*?)}", template),
+        use_fixed_model_version=use_fixed_model_version,
     )
     input_values = {
         "ill_formed_output": ill_formed_output.content,
@@ -458,6 +466,8 @@ async def agenerate(
     output_parser: BaseOutputParser[OutputType],
     temperature: float = 0.7,
     structured_output: bool = False,
+    bad_output_process_model: str = DEFAULT_BAD_OUTPUT_PROCESS_MODEL,
+    use_fixed_model_version: bool = True,
 ) -> OutputType:
     input_variables = re.findall(
         r"(?<!{){([^{}]+)}(?!})", template
@@ -473,6 +483,7 @@ async def agenerate(
         template=template,
         input_variables=input_variables,
         temperature=temperature,
+        use_fixed_model_version=use_fixed_model_version,
     )
 
     if "format_instructions" not in input_values:
@@ -516,7 +527,10 @@ async def agenerate(
             extra={"markup": True},
         )
         reformat_parsed_result = format_bad_output(
-            result, format_instructions=output_parser.get_format_instructions()
+            result,
+            format_instructions=output_parser.get_format_instructions(),
+            model_name=bad_output_process_model,
+            use_fixed_model_version=use_fixed_model_version,
         )
         parsed_result = output_parser.invoke(reformat_parsed_result)
     log.info(f"Generated result: {parsed_result}")
@@ -530,6 +544,8 @@ async def agenerate_env_profile(
     inspiration_prompt: str = "asking my boyfriend to stop being friends with his ex",
     examples: str = "",
     temperature: float = 0.7,
+    bad_output_process_model: str = DEFAULT_BAD_OUTPUT_PROCESS_MODEL,
+    use_fixed_model_version: bool = True,
 ) -> tuple[EnvironmentProfile, str]:
     """
     Using langchain to generate the background
@@ -549,6 +565,8 @@ async def agenerate_env_profile(
         ),
         output_parser=PydanticOutputParser(pydantic_object=EnvironmentProfile),
         temperature=temperature,
+        bad_output_process_model=bad_output_process_model,
+        use_fixed_model_version=use_fixed_model_version,
     )
 
 
@@ -556,6 +574,8 @@ async def agenerate_env_profile(
 async def agenerate_relationship_profile(
     model_name: str,
     agents_profiles: list[str],
+    bad_output_process_model: str = DEFAULT_BAD_OUTPUT_PROCESS_MODEL,
+    use_fixed_model_version: bool = True,
 ) -> tuple[RelationshipProfile, str]:
     """
     Using langchain to generate the background
@@ -572,6 +592,8 @@ async def agenerate_relationship_profile(
             agent_profile=agent_profile,
         ),
         output_parser=PydanticOutputParser(pydantic_object=RelationshipProfile),
+        bad_output_process_model=bad_output_process_model,
+        use_fixed_model_version=use_fixed_model_version,
     )
 
 
@@ -586,6 +608,8 @@ async def agenerate_action(
     goal: str,
     temperature: float = 0.7,
     script_like: bool = False,
+    bad_output_process_model: str = DEFAULT_BAD_OUTPUT_PROCESS_MODEL,
+    use_fixed_model_version: bool = True,
 ) -> AgentAction:
     """
     Using langchain to generate an example episode
@@ -635,6 +659,8 @@ async def agenerate_action(
             ),
             output_parser=PydanticOutputParser(pydantic_object=AgentAction),
             temperature=temperature,
+            bad_output_process_model=bad_output_process_model,
+            use_fixed_model_version=use_fixed_model_version,
         )
     except Exception:
         return AgentAction(action_type="none", argument="")
@@ -650,6 +676,8 @@ async def agenerate_script(
     agent_name: str = "",
     history: str = "",
     single_step: bool = False,
+    bad_output_process_model: str = DEFAULT_BAD_OUTPUT_PROCESS_MODEL,
+    use_fixed_model_version: bool = True,
 ) -> tuple[ScriptInteractionReturnType, str]:
     """
     Using langchain to generate an the script interactions between two agent
@@ -683,6 +711,8 @@ async def agenerate_script(
                     single_turn=True,
                 ),
                 temperature=temperature,
+                bad_output_process_model=bad_output_process_model,
+                use_fixed_model_version=use_fixed_model_version,
             )
 
         else:
@@ -705,6 +735,8 @@ async def agenerate_script(
                     single_turn=False,
                 ),
                 temperature=temperature,
+                bad_output_process_model=bad_output_process_model,
+                use_fixed_model_version=use_fixed_model_version,
             )
     except Exception as e:
         # TODO raise(e) # Maybe we do not want to return anything?
@@ -733,7 +765,12 @@ def process_history(
 
 
 @beartype
-async def agenerate_init_profile(model_name: str, basic_info: dict[str, str]) -> str:
+async def agenerate_init_profile(
+    model_name: str,
+    basic_info: dict[str, str],
+    bad_output_process_model: str = DEFAULT_BAD_OUTPUT_PROCESS_MODEL,
+    use_fixed_model_version: bool = True,
+) -> str:
     """
     Using langchain to generate the background
     """
@@ -767,11 +804,19 @@ async def agenerate_init_profile(model_name: str, basic_info: dict[str, str]) ->
             secret=basic_info["secret"],
         ),
         output_parser=StrOutputParser(),
+        bad_output_process_model=bad_output_process_model,
+        use_fixed_model_version=use_fixed_model_version,
     )
 
 
 @beartype
-async def convert_narratives(model_name: str, narrative: str, text: str) -> str:
+async def convert_narratives(
+    model_name: str,
+    narrative: str,
+    text: str,
+    bad_output_process_model: str = DEFAULT_BAD_OUTPUT_PROCESS_MODEL,
+    use_fixed_model_version: bool = True,
+) -> str:
     if narrative == "first":
         return await agenerate(
             model_name=model_name,
@@ -780,6 +825,8 @@ async def convert_narratives(model_name: str, narrative: str, text: str) -> str:
             {text}""",
             input_values=dict(text=text),
             output_parser=StrOutputParser(),
+            bad_output_process_model=bad_output_process_model,
+            use_fixed_model_version=use_fixed_model_version,
         )
     elif narrative == "second":
         return await agenerate(
@@ -789,13 +836,20 @@ async def convert_narratives(model_name: str, narrative: str, text: str) -> str:
             {text}""",
             input_values=dict(text=text),
             output_parser=StrOutputParser(),
+            bad_output_process_model=bad_output_process_model,
+            use_fixed_model_version=use_fixed_model_version,
         )
     else:
         raise ValueError(f"Narrative {narrative} is not supported.")
 
 
 @beartype
-async def agenerate_goal(model_name: str, background: str) -> str:
+async def agenerate_goal(
+    model_name: str,
+    background: str,
+    bad_output_process_model: str = DEFAULT_BAD_OUTPUT_PROCESS_MODEL,
+    use_fixed_model_version: bool = True,
+) -> str:
     """
     Using langchain to generate the background
     """
@@ -806,4 +860,6 @@ async def agenerate_goal(model_name: str, background: str) -> str:
             """,
         input_values=dict(background=background),
         output_parser=StrOutputParser(),
+        bad_output_process_model=bad_output_process_model,
+        use_fixed_model_version=use_fixed_model_version,
     )