Change 3rd layer prompt ## FALALCY from in context learning to dialog simulation

utils/core.py

@@ -49,6 +49,7 @@ class HamburgerStyle:
                 "use_cache": False,
             },
         )
+        self.chat_model = ChatHuggingFace(llm=self.llm)
         self.flicc_model = "fzanartu/flicc"
         self.card_model = "crarojasca/BinaryAugmentedCARDS"
         self.semantic_textual_similarity = "sentence-transformers/all-MiniLM-L6-v2"
@@ -61,10 +62,8 @@ class HamburgerStyle:
         ## FACT: ReAct
         prompt = REACT
 
-        chat_model = ChatHuggingFace(llm=self.llm)
-
         # define the agent
-        chat_model_with_stop = chat_model.bind(stop=["\nObservation"])
+        chat_model_with_stop = self.chat_model.bind(stop=["\nObservation"])
         agent = (
             {
                 "input": lambda x: x["input"],
@@ -73,7 +72,7 @@ class HamburgerStyle:
                 ),
             }
             | prompt
-            | chat_model
+            | self.chat_model
             | ReActJsonSingleInputOutputParser()
         )
 
@@ -105,13 +104,13 @@ class HamburgerStyle:
         ## FALLACY: Fallacy
 
         # 1 predict fallacy label in FLICC taxonomy
-        fallacy_label = self.endpoint_query(
+        detected_fallacy = self.endpoint_query(
             model=self.flicc_model, payload=misinformation
         )[0][0].get("label")
-        fallacy_definition = DEFINITIONS.get(fallacy_label)
+        fallacy_definition = DEFINITIONS.get(detected_fallacy)
 
         # 2 get all examples with the same label
-        claims = FALLACY_CLAIMS.get(fallacy_label, None)
+        claims = FALLACY_CLAIMS.get(detected_fallacy, None)
 
         # 3 get cosine similarity for all claims and myth
         example_myths = self.endpoint_query(
@@ -125,18 +124,18 @@ class HamburgerStyle:
         example_response = DEBUNKINGS.get(claims[max_similarity])
         fact = re.findall(r"## FALLACY:.*?(?=##)", example_response, re.DOTALL)[
             0
-        ]  # get only the fallacy layer if the example.
+        ]  # get only the fallacy layer from the example.
         fact = fact.replace("## FALLACY:", "")
 
         prompt = INCONTEXT
-        chain = prompt | self.llm
+        chain = prompt | self.chat_model
         return chain.invoke(
             {
                 "misinformation": misinformation,
-                "example_myth": example_myth,
-                "example_response": fact,
-                "fallacy": fallacy_label,
+                "detected_fallacy": detected_fallacy,
                 "fallacy_definition": fallacy_definition,
+                "Response": fact,
+                "example_myth": example_myth,
                 "factual_information": self.hamburger[1].content,
             }
         )

utils/templates.py

@@ -32,27 +32,22 @@ Thought:{agent_scratchpad}"""
 )
 
 INCONTEXT = PromptTemplate.from_template(
-    """[Role]:
-As an expert climate analyst, your role is to provide precise and concise responses to climate change misinformation.
-
-[Instruction]:
-Identify the logical or argumentative fallacy within 40 words or fewer. Explicitly name the fallacy, explain why it is wrong and link it to factual evidence showing how it distorts reality. 
-
-[In-context example]:
-Consider the following example: 
-misinformation: {example_myth}
-response: {example_response}
-
-[Misinformation]:
-You've identified a {fallacy}-based fallacy in the following misinformation:
-{fallacy} definition: {fallacy_definition}
-
-You also possess the following factual information: {factual_information}
-<<<
-misinformation: {misinformation}
-response:
->>>
-"""
+    """<s>[INST] <<SYS>>
+You are a senior climate analyst, an expert in identifying and responding to climate change misinformation.
+<</SYS>>
+What fallacy is contained in the following misinformation?
+misinformation: {misinformation} [/INST] 
+Your text contains {detected_fallacy} fallacy. {detected_fallacy} fallacy is {fallacy_definition} </s>
+<s>[INST] What is the factual evidence surrounding this misinformation?[/INST]
+{factual_information}</s>
+<s>[INST] Provide a precise and concise response to this climate change misinformation.
+1. Identify the logical or argumentative fallacy in 40 words or fewer.
+2. Explicitly name the fallacy, explain why it's incorrect, and link it to factual evidence showing how it distorts reality.
+Consider the following example before providing your answer:
+Misinformation: {example_myth}
+Response: {example_response}
+Misinformation: {misinformation}
+Response:[/INST]"""
 )
 
 SUMMARIZATION = PromptTemplate.from_template(