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CommonsenseMCQA

aisteer360.evaluation.use_cases.commonsense_mcqa

Use case class for the commonsense multiple-choice question answering (MCQA) task.

use_case

CommonsenseMCQA

Bases: UseCase

Commonsense MCQA evaluation use case.

Evaluates model's ability to answer commonsense questions via accuracy on the CommonsenseMCQA dataset (https://huggingface.co/datasets/tau/commonsense_qa). Supports answer choice shuffling across multiple runs to reduce position bias and improve evaluation robustness.

The evaluation data should contain questions with multiple choice options where models are asked to respond with only the letter (A, B, C, etc.) corresponding to their chosen answer.

Attributes:

Name Type Description
num_shuffling_runs int

Number of times to shuffle answer choices for each question to mitigate position bias effects.
Source code in aisteer360/evaluation/use_cases/commonsense_mcqa/use_case.py 
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class CommonsenseMCQA(UseCase):
    """Commonsense MCQA evaluation use case.

    Evaluates model's ability to answer commonsense questions via accuracy on the CommonsenseMCQA dataset
    ([https://huggingface.co/datasets/tau/commonsense_qa](https://huggingface.co/datasets/tau/commonsense_qa)). Supports
    answer choice shuffling across multiple runs to reduce position bias and improve evaluation robustness.

    The evaluation data should contain questions with multiple choice options where models are asked to respond with
    only the letter (A, B, C, etc.) corresponding to their chosen answer.

    Attributes:
        num_shuffling_runs: Number of times to shuffle answer choices for each question to mitigate position bias effects.
    """
    num_shuffling_runs: int

    def validate_evaluation_data(self, evaluation_data: dict[str, Any]):
        """Validates that evaluation data contains required fields for MCQA evaluation.

        Ensures each data instance has the necessary keys and non-null values for the evaluation.

        Args:
            evaluation_data: Dictionary containing a single evaluation instance with question, answer choices, and correct answer information.

        Raises:
            ValueError: If required keys ('id', 'question', 'answer', 'choices') are missing or if any required fields contain null/NaN values.
        """
        if "id" not in evaluation_data.keys():
            raise ValueError("The evaluation data must include an 'id' key")

        missing_keys = [col for col in _EVALUATION_REQ_KEYS if col not in evaluation_data.keys()]
        if missing_keys:
            raise ValueError(f"Missing required keys: {missing_keys}")

        if any(
            key not in evaluation_data or evaluation_data[key] is None or
            (isinstance(evaluation_data[key], float) and math.isnan(evaluation_data[key]))
            for key in _EVALUATION_REQ_KEYS
        ):
            raise ValueError("Some required fields are missing or null.")

    def generate(
        self,
        model_or_pipeline,
        tokenizer,
        gen_kwargs: dict | None = None,
        runtime_overrides: dict[tuple[str, str], str] | None = None
    ) -> list[dict[str, Any]]:
        """Generates model responses for multiple-choice questions with shuffled answer orders.

        Creates prompts for each question with shuffled answer choices, generates model responses, and parses the
        outputs to extract letter choices. Repeats the process multiple times with different answer orderings to reduce
        positional bias.

        Args:
            model_or_pipeline: Either a HuggingFace model or SteeringPipeline instance to use for generation.
            tokenizer: Tokenizer for encoding/decoding text.
            gen_kwargs: Optional generation parameters.
            runtime_overrides: Optional runtime parameter overrides for steering controls, structured as {(pipeline_name, param_name): value}.

        Returns:
            List of generation dictionaries, each containing:

                - "response": Parsed letter choice (A, B, C, etc.) or None if not parseable
                - "prompt": Full prompt text sent to the model
                - "question_id": Identifier from the original evaluation data
                - "reference_answer": Correct letter choice for this shuffled ordering

        Note:

        - The number of returned generations will be `len(evaluation_data)` * `num_shuffling_runs` due to answer choice shuffling.
        """

        if not self.evaluation_data:
            print('No evaluation data provided.')
            return []
        gen_kwargs = dict(gen_kwargs or {})

        # form prompt data
        prompt_data = []
        for instance in self.evaluation_data:
            data_id = instance['id']
            question = instance['question']
            answer = instance['answer']
            choices = instance['choices']
            # shuffle order of choices for each shuffling run
            for _ in range(self.num_shuffling_runs):

                lines = ["You will be given a multiple-choice question and asked to select from a set of choices."]
                lines += [f"\nQuestion: {question}\n"]

                # shuffle
                choice_order = list(range(len(choices)))
                random.shuffle(choice_order)
                for i, old_idx in enumerate(choice_order):
                    lines.append(f"{_LETTERS[i]}. {choices[old_idx]}")

                lines += ["\nPlease only print the letter corresponding to your choice."]
                lines += ["\nAnswer:"]

                prompt_data.append(
                    {
                        "id": data_id,
                        "prompt": "\n".join(lines),
                        "reference_answer": _LETTERS[choice_order.index(choices.index(answer))]
                    }
                )

        # batch template/generate/decode
        choices = batch_retry_generate(
            prompt_data=prompt_data,
            model_or_pipeline=model_or_pipeline,
            tokenizer=tokenizer,
            parse_fn=self._parse_letter,
            gen_kwargs=gen_kwargs,
            runtime_overrides=runtime_overrides,
            evaluation_data=self.evaluation_data
        )

        # store
        generations = [
            {
                "response": choice,
                "prompt": prompt_dict["prompt"],
                "question_id": prompt_dict["id"],
                "reference_answer": prompt_dict["reference_answer"],
            }
            for prompt_dict, choice in zip(prompt_data, choices)
        ]

        return generations

    def evaluate(self, generations: list[dict[str, Any]]) -> dict[str, dict[str, Any]]:
        """Evaluates generated responses against reference answers using configured metrics.

        Extracts responses and reference answers from generations and computes scores using all evaluation metrics
        specified during initialization.

        Args:
            generations: List of generation dictionaries returned by the `generate()` method, each containing response,
                reference_answer, and question_id fields.

        Returns:
            Dictionary of scores keyed by `metric_name`
        """
        eval_data = {
            "responses": [generation["response"] for generation in generations],
            "reference_answers": [generation["reference_answer"] for generation in generations],
            "question_ids": [generation["question_id"] for generation in generations],
        }

        scores = {}
        for metric in self.evaluation_metrics:
            scores[metric.name] = metric(**eval_data)

        return scores

    def export(self, profiles: dict[str, Any], save_dir) -> None:
        """Exports evaluation profiles to (tabbed) JSON format."""

        with open(Path(save_dir) / "profiles.json", "w", encoding="utf-8") as f:
            json.dump(profiles, f, indent=4, ensure_ascii=False)

    @staticmethod
    def _parse_letter(response) -> str:
        """Extracts the letter choice from model's generation.

        Parses model output to find the first valid letter (A-Z) that represents the model's choice.

        Args:
            response: Raw text response from the model.

        Returns:
            Single uppercase letter (A, B, C, etc.) representing the model's choice, or None if no valid letter choice could be parsed.
        """
        valid = _LETTERS
        text = re.sub(r"^\s*(assistant|system|user)[:\n ]*", "", response, flags=re.I).strip()
        match = re.search(rf"\b([{valid}])\b", text, flags=re.I)
        return match.group(1).upper() if match else None
evaluation_data = [(json.loads(line)) for line in f] if path.suffix == '.jsonl' else json.load(f) instance-attribute
evaluation_metrics = evaluation_metrics instance-attribute
num_shuffling_runs instance-attribute
evaluate(generations)

Evaluates generated responses against reference answers using configured metrics.

Extracts responses and reference answers from generations and computes scores using all evaluation metrics specified during initialization.

Parameters:

Name Type Description Default
generations list[dict[str, Any]]

List of generation dictionaries returned by the generate() method, each containing response, reference_answer, and question_id fields.

 required

Returns:

Type Description
dict[str, dict[str, Any]]

Dictionary of scores keyed by metric_name
Source code in aisteer360/evaluation/use_cases/commonsense_mcqa/use_case.py 
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def evaluate(self, generations: list[dict[str, Any]]) -> dict[str, dict[str, Any]]:
    """Evaluates generated responses against reference answers using configured metrics.

    Extracts responses and reference answers from generations and computes scores using all evaluation metrics
    specified during initialization.

    Args:
        generations: List of generation dictionaries returned by the `generate()` method, each containing response,
            reference_answer, and question_id fields.

    Returns:
        Dictionary of scores keyed by `metric_name`
    """
    eval_data = {
        "responses": [generation["response"] for generation in generations],
        "reference_answers": [generation["reference_answer"] for generation in generations],
        "question_ids": [generation["question_id"] for generation in generations],
    }

    scores = {}
    for metric in self.evaluation_metrics:
        scores[metric.name] = metric(**eval_data)

    return scores
export(profiles, save_dir)

Exports evaluation profiles to (tabbed) JSON format.

Source code in aisteer360/evaluation/use_cases/commonsense_mcqa/use_case.py 
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def export(self, profiles: dict[str, Any], save_dir) -> None:
    """Exports evaluation profiles to (tabbed) JSON format."""

    with open(Path(save_dir) / "profiles.json", "w", encoding="utf-8") as f:
        json.dump(profiles, f, indent=4, ensure_ascii=False)
generate(model_or_pipeline, tokenizer, gen_kwargs=None, runtime_overrides=None)

Generates model responses for multiple-choice questions with shuffled answer orders.

Creates prompts for each question with shuffled answer choices, generates model responses, and parses the outputs to extract letter choices. Repeats the process multiple times with different answer orderings to reduce positional bias.

Parameters:

Name Type Description Default
model_or_pipeline

Either a HuggingFace model or SteeringPipeline instance to use for generation.

 required
tokenizer

Tokenizer for encoding/decoding text.

 required
gen_kwargs dict | None

Optional generation parameters.

 None
runtime_overrides dict[tuple[str, str], str] | None

Optional runtime parameter overrides for steering controls, structured as {(pipeline_name, param_name): value}.

 None

Returns:

Type Description
list[dict[str, Any]]

List of generation dictionaries, each containing:

  • "response": Parsed letter choice (A, B, C, etc.) or None if not parseable
  • "prompt": Full prompt text sent to the model
  • "question_id": Identifier from the original evaluation data
  • "reference_answer": Correct letter choice for this shuffled ordering

Note:

  • The number of returned generations will be len(evaluation_data) * num_shuffling_runs due to answer choice shuffling.
Source code in aisteer360/evaluation/use_cases/commonsense_mcqa/use_case.py 
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def generate(
    self,
    model_or_pipeline,
    tokenizer,
    gen_kwargs: dict | None = None,
    runtime_overrides: dict[tuple[str, str], str] | None = None
) -> list[dict[str, Any]]:
    """Generates model responses for multiple-choice questions with shuffled answer orders.

    Creates prompts for each question with shuffled answer choices, generates model responses, and parses the
    outputs to extract letter choices. Repeats the process multiple times with different answer orderings to reduce
    positional bias.

    Args:
        model_or_pipeline: Either a HuggingFace model or SteeringPipeline instance to use for generation.
        tokenizer: Tokenizer for encoding/decoding text.
        gen_kwargs: Optional generation parameters.
        runtime_overrides: Optional runtime parameter overrides for steering controls, structured as {(pipeline_name, param_name): value}.

    Returns:
        List of generation dictionaries, each containing:

            - "response": Parsed letter choice (A, B, C, etc.) or None if not parseable
            - "prompt": Full prompt text sent to the model
            - "question_id": Identifier from the original evaluation data
            - "reference_answer": Correct letter choice for this shuffled ordering

    Note:

    - The number of returned generations will be `len(evaluation_data)` * `num_shuffling_runs` due to answer choice shuffling.
    """

    if not self.evaluation_data:
        print('No evaluation data provided.')
        return []
    gen_kwargs = dict(gen_kwargs or {})

    # form prompt data
    prompt_data = []
    for instance in self.evaluation_data:
        data_id = instance['id']
        question = instance['question']
        answer = instance['answer']
        choices = instance['choices']
        # shuffle order of choices for each shuffling run
        for _ in range(self.num_shuffling_runs):

            lines = ["You will be given a multiple-choice question and asked to select from a set of choices."]
            lines += [f"\nQuestion: {question}\n"]

            # shuffle
            choice_order = list(range(len(choices)))
            random.shuffle(choice_order)
            for i, old_idx in enumerate(choice_order):
                lines.append(f"{_LETTERS[i]}. {choices[old_idx]}")

            lines += ["\nPlease only print the letter corresponding to your choice."]
            lines += ["\nAnswer:"]

            prompt_data.append(
                {
                    "id": data_id,
                    "prompt": "\n".join(lines),
                    "reference_answer": _LETTERS[choice_order.index(choices.index(answer))]
                }
            )

    # batch template/generate/decode
    choices = batch_retry_generate(
        prompt_data=prompt_data,
        model_or_pipeline=model_or_pipeline,
        tokenizer=tokenizer,
        parse_fn=self._parse_letter,
        gen_kwargs=gen_kwargs,
        runtime_overrides=runtime_overrides,
        evaluation_data=self.evaluation_data
    )

    # store
    generations = [
        {
            "response": choice,
            "prompt": prompt_dict["prompt"],
            "question_id": prompt_dict["id"],
            "reference_answer": prompt_dict["reference_answer"],
        }
        for prompt_dict, choice in zip(prompt_data, choices)
    ]

    return generations
validate_evaluation_data(evaluation_data)

Validates that evaluation data contains required fields for MCQA evaluation.

Ensures each data instance has the necessary keys and non-null values for the evaluation.

Parameters:

Name Type Description Default
evaluation_data dict[str, Any]

Dictionary containing a single evaluation instance with question, answer choices, and correct answer information.

 required

Raises:

Type Description
ValueError

If required keys ('id', 'question', 'answer', 'choices') are missing or if any required fields contain null/NaN values.
Source code in aisteer360/evaluation/use_cases/commonsense_mcqa/use_case.py 
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def validate_evaluation_data(self, evaluation_data: dict[str, Any]):
    """Validates that evaluation data contains required fields for MCQA evaluation.

    Ensures each data instance has the necessary keys and non-null values for the evaluation.

    Args:
        evaluation_data: Dictionary containing a single evaluation instance with question, answer choices, and correct answer information.

    Raises:
        ValueError: If required keys ('id', 'question', 'answer', 'choices') are missing or if any required fields contain null/NaN values.
    """
    if "id" not in evaluation_data.keys():
        raise ValueError("The evaluation data must include an 'id' key")

    missing_keys = [col for col in _EVALUATION_REQ_KEYS if col not in evaluation_data.keys()]
    if missing_keys:
        raise ValueError(f"Missing required keys: {missing_keys}")

    if any(
        key not in evaluation_data or evaluation_data[key] is None or
        (isinstance(evaluation_data[key], float) and math.isnan(evaluation_data[key]))
        for key in _EVALUATION_REQ_KEYS
    ):
        raise ValueError("Some required fields are missing or null.")