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Feature request: add more MCQA benchmarks #919

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@penguinwang96825

Hi! evalscope is amazingly good, I'm honestly so obsessed with adding benchmarks lately.

I noticed that there are many multiple-choice QA (MCQA) datasets that are widely used in literature but haven’t yet been added to evalscope. Some examples include:

We've run these datasets ourselves and confirmed that the implementations are correct. Below is a quick summary table of results (subset of benchmarks I mentioned):

Dataset Llama3-8B Llama3.1-8B Qwen2.5-3B Qwen2.5-7B Qwen2.5-14B
SiQA 0.7185 0.7236 0.7298 0.7805 0.7856
PiQA 0.8063 0.8270 0.7715 0.8705 0.8917
CommonsenseQA 0.7453 0.7420 0.7559 0.8206 0.8206
LogiQA 0.4363 0.4347 0.4347 0.5376 0.6160
QASC 0.7624 0.7959 0.7203 0.7959 0.8294
PubMedQA 0.6300 0.7090 0.5840 0.5360 0.4480
MRI-MCQA 0.5258 0.5488 0.4938 0.5595 0.6004
MathQA 0.5528 0.6945 0.7725 0.8328 0.8516
HaluEval-D 0.8210 0.8022 0.5219 0.9087 0.9404
HaluEval-Q 0.7898 0.6833 0.4914 0.6733 0.8839
HaluEval-S 0.7174 0.6552 0.4545 0.5875 0.8490

Would you be interested in adding these benchmarks to evalscope? I think they’d really strengthen coverage for reasoning and domain-specific evaluation. If positive, I'll submit a PR.

Warm regards,
Yang

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  1. Yunnglin commented on Oct 26, 2025

    @Yunnglin
    Collaborator

    Thank you for this excellent proposal! I'm very interested in adding these MCQA benchmarks to evalscope.

    The datasets you've identified would significantly strengthen our coverage for reasoning and domain-specific evaluation. I really appreciate that you've already validated the implementations and provided the comprehensive benchmark results.

    Please go ahead and submit a PR - we'd love to have these benchmarks included!

    Thanks again for your contribution to the project.

  2. penguinwang96825 commented on Oct 27, 2025

    @penguinwang96825
    ContributorAuthor

    Since PR #923 has resolved this issue, I'll close it for now. Thanks for all the support!

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