[EVAL] MultiChallenge

src/lighteval/tasks/tasks/multi_challenge.py

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+"""
+name:
+MultiChallenge
+
+dataset:
+nmayorga7/multichallenge
+
+abstract:
+MultiChallenge evaluates large language models (LLMs) on their ability to
+conduct multi-turn conversations with human users.
+The model is given a target question belonging to one or
+more axes (categories) and must provide a free-form answer.
+The evaluation uses a secondary judge model to determine if the
+answer satisfies the pass criteria for that question.
+
+languages:
+english
+
+tags:
+conversational, generation, instruction-following
+
+paper:
+https://arxiv.org/abs/2501.17399
+"""
+
+from inspect_ai.dataset import Sample
+from inspect_ai.model._chat_message import ChatMessageAssistant, ChatMessageUser
+from inspect_ai.scorer import Score, Target, accuracy, model_graded_fact, scorer, stderr
+from inspect_ai.solver import Generate, TaskState, generate, solver
+
+from lighteval.tasks.lighteval_task import LightevalTaskConfig
+from lighteval.tasks.requests import Doc
+
+
+JUDGE_PROMPT = """You are tasked with evaluating a model response to see if it meets a specific criteria.
+The criteria will always be YES/NO evaluation.
+
+The model response is as follows:
+<MODEL_RESPONSE>
+{answer}
+</MODEL_RESPONSE>
+
+The criteria that the model response must meet is as follows. Be VERY STRICT!:
+<CRITERIA>
+{criterion}
+</CRITERIA>
+
+Print your reasoning followed by your verdict, either "YES" or "NO"."""
+
+
+def format_conversation(conversation: list[dict]) -> str:
+    """Format conversation messages into a single string for model input."""
+    formatted_messages = []
+    for msg in conversation:
+        role = msg["role"].upper()
+        content = msg["content"]
+        formatted_messages.append(f"{role}:\n{content}")
+
+    return "\n\n".join(formatted_messages)
+
+
+def multi_challenge_prompt(line, task_name: str = None):
+    """Convert dataset to Doc object"""
+
+    conversation = line["CONVERSATION"]
+    formatted_conv = format_conversation(conversation)
+    return Doc(
+        task_name=task_name,
+        query=formatted_conv,
+        instruction=None,
+        specific={
+            "question_id": line["QUESTION_ID"],
+            "axis": line["AXIS"],
+            "target_question": line["TARGET_QUESTION"],
+            "pass_criteria": line["PASS_CRITERIA"],
+            "conversation": conversation,
+        },
+    )
+
+
+base_scorer = model_graded_fact(
+    template=JUDGE_PROMPT,
+    grade_pattern=r"\b(YES|NO)\b",
+    model="openai/gpt-4o-2024-08-06",
+)
+
+
+@scorer(metrics=[accuracy(), stderr()])
+def multi_challenge_scorer():
+    async def score(state: TaskState, target: Target):
+        score = await base_scorer(state, target)
+        judge_verdict = score.value.upper() if score.value else None
+
+        if not judge_verdict or judge_verdict not in ["YES", "NO"]:
+            return Score(
+                value="I",
+                answer=score.answer,
+                explanation=f"Could not extract valid verdict from judge output: {score.explanation}",
+            )
+
+        pass_criteria = state.metadata.get("pass_criteria", "YES")
+        passed = judge_verdict == pass_criteria
+
+        return Score(
+            value="C" if passed else "I",
+            answer=score.answer,
+            explanation=score.explanation,
+        )
+
+    return score
+
+
+@solver
+def conversation_solver():
+    """Solver that builds conversation history from metadata."""
+
+    async def solve(state: TaskState, generate: Generate):
+        conversation = state.metadata.get("conversation", [])
+
+        state.messages = []
+
+        for msg in conversation:
+            role = msg["role"].lower()
+            content = msg["content"]
+
+            if role == "user":
+                state.messages.append(ChatMessageUser(content=content))
+            elif role == "assistant":
+                state.messages.append(ChatMessageAssistant(content=content))
+
+        return state
+
+    return solve
+
+
+def record_to_sample(record: dict) -> Sample:
+    """Convert dataset record to inspect-ai Sample object."""
+    conversation = record["CONVERSATION"]
+
+    last_msg = None
+    for msg in reversed(conversation):
+        if msg["role"] == "user":
+            last_msg = msg["content"]
+            break
+
+    return Sample(
+        input=last_msg or "",
+        target=record["TARGET_QUESTION"],
+        metadata={
+            "question_id": record["QUESTION_ID"],
+            "axis": record["AXIS"],
+            "pass_criteria": record["PASS_CRITERIA"],
+            "conversation": conversation,
+            "length": len(conversation),
+        },
+    )
+
+
+multi_challenge = LightevalTaskConfig(
+    name="multi_challenge",
+    prompt_function=multi_challenge_prompt,
+    hf_repo="nmayorga7/multichallenge",
+    hf_subset="default",
+    hf_avail_splits=["train"],
+    evaluation_splits=["train"],
+    few_shots_split=None,
+    few_shots_select=None,
+    generation_size=2048,
+    stop_sequence=[],
+    version=0,
+    sample_fields=record_to_sample,
+    metrics=[],  # Metrics are defined in the scorer decorator for inspect-ai tasks
+    solver=[conversation_solver(), generate(cache=True)],
+    scorer=multi_challenge_scorer(),
+)
+
+TASKS_TABLE = [multi_challenge]