MMLongBench-Doc-V2 Improves Long Document QA Benchmark Accuracy
Key takeaways
- Benchmark quality significantly impacts the perceived performance of LLMs.
- MMLongBench-Doc-V2 offers a more accurate and semantically aware evaluation for long-document QA.
- Correcting ground-truth annotations is vital for reliable benchmark results.
- LLM judges can provide more nuanced and semantically robust evaluation than string matching.
Who benefits
Summary
MMLongBench-Doc-V2 is a revised long-document QA benchmark that corrects 106 ground-truth annotations and replaces string-based answer comparison with an LLM judge for semantic evaluation. These changes address issues in the original benchmark that skewed performance measurements for long-context language models.
Why it matters
Accurate benchmarks are crucial for developing and comparing long-context language models; this revision ensures more reliable and meaningful evaluation of their capabilities.
How to implement this in your domain
- 1Review current LLM evaluation pipelines to ensure they use up-to-date and semantically robust benchmarks.
- 2Integrate MMLongBench-Doc-V2 into evaluation suites for long-document QA models.
- 3Adopt LLM-based semantic judges for evaluating model outputs, moving beyond strict string matching.
- 4Analyze model performance on the revised benchmark to identify true strengths and weaknesses in long-context understanding.
- 5Contribute to community efforts for benchmark improvement by reporting issues and suggesting corrections.
Original post by Mingtian Zhang
"arXiv:2608.03397v1 Announce Type: new Abstract: MMLongBench-Doc is a long-document QA benchmark of 1,082 questions over 135 PDFs. Two properties of it push measured scores away from the quantity they are meant to capture: the reference metric compares extracted answers, so 1,358,…"
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Originally posted by Mingtian Zhang on X · view source
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