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Implement MCQEval

Our built-in MCQEval framework serves as an evaluation task factory. To implement, define two components and let openbench take care of the rest!

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Component 1: Map Your Dataset to MCQ Samples

Define a mapping for dataset records to fit the expected MCQSample format: 
class MCQSample(Sample):
    input: str | list[ChatMessage]
    target: str     # must be a single uppercase letter (e.g. "A")
evals/openbookqa.py
def record_to_mcq_sample(record) -> MCQSample:
    """Convert an OpenBookQA record to an openbench MCQSample."""

    question = record["question_stem"]
    options = [choice for choice in record["choices"]["text"]]
    prompt = create_dynamic_multiple_choice_prompt(question, options)

    return MCQSample(
        input=prompt,
        target=record["answerKey"],
        id=record.get("id"),
        metadata={"choice_labels": record["choices"]["label"]},
    )

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Component 2: Define Your Eval Task

Given a record_to_mcq_sample function, MCQEval will spin up an InspectAI Task: 
@task
def openbookqa(split: str = "validation") -> Task:
    """OpenBookQA multiple choice science question evaluation (MCQ Abstracted)."""

    valid_splits = ["train", "validation", "test"]
    if split not in valid_splits:
        raise ValueError(f"Invalid split '{split}'. Must be one of {valid_splits}")

    return MCQEval(
        # required fields
        name="openbookqa",
        dataset_path="allenai/openbookqa",
        record_to_mcq_sample=record_to_mcq_sample,
        split=split,
    )
And that’s it! After registering your new MCQ task in the registry, you can run it with bench eval!

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Additional Info

MCQEval accepts additional configuration parameters: 
def MCQEval(
    *,
    name: str,                                        # Task name
    dataset_type: str = "hf"                          # "hf", "csv", or "json"
    dataset_path: str,                                # Hugging Face dataset path/name
    record_to_mcq_sample,                             # Function converting a raw record into an `MCQSample`
    split: str,                                       # HF dataset split (e.g., "train", "validation", "test")
    auto_id: bool = True,                             # Auto-generate IDs for samples when true
    subset_name: Optional[str] = None,                # Dataset subset to load
    group_keys: Optional[List[str]] = None,           # Optional metadata keys to group reported metrics
    additional_metrics: Optional[List[Any]] = None,   # Optional additional metrics
    prompt_template: Optional[str] = None,            # Optional system prompt
    config: Optional[GenerateConfig] = None,          # Optional model `GenerateConfig`
    epochs: Optional[Epochs] = None,                  # Optional `Epochs` to repeat samples and reduce scores
    dataset_kwargs: Optional[dict[str, Any]] = None,  # Additional dataset-specific parameters
) -> Task
When called, MCQEval loads the dataset type (default hf) according to the provided mapping function, e.g., 
dataset = hf_dataset(
    dataset_path,
    split=split,
    sample_fields=record_to_mcq_sample,
    auto_id=auto_id,
    name=subset_name,  # subset name
    **(dataset_kwargs or {}),
)
… defines a basic generation solver, 
solver = [generate()]
if prompt_template:
    solver = [system_message(prompt_template), generate()]
… creates a dynamic mcq scorer, 
scorer = create_mcq_scorer(
    group_keys=group_keys,
    additional_metrics=additional_metrics,
)()
… and returns a packaged evaluation task. 
return Task(
    name=name,
    dataset=dataset,
    solver=solver,
    scorer=scorer,
    config=config if config else GenerateConfig(),
    epochs=epochs,
)