Paper Audits Measurement Bias in FLARE Finance Benchmark Scoring Pipeline
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Dung Cao, Palaash Gang, and Arjun Veluri released an audit study pointing out measurement bias in the scoring pipeline of the FLARE financial task suite: a considerable portion of scoring results are determined by answer extraction rules rather than the actual capabilities of the tested models. FLARE is the financial task suite behind PIXIU and FinBen. Its forked lm-evaluation-harness applies different first-match judgment rules to the same generation results, with accuracy differences reaching up to 0.4216, and no leaderboard specifies which set of rules it uses.
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- arXiv q-finAudit Study Finds That Scores in the FLARE Financial Large Model Benchmark Are Partly Determined by Answer Extraction Rules Rather Than Model Capability
This 12-page arXiv paper audits the scoring pipeline of the FLARE financial task suite, finding that a substantial portion of scoring cell results are determined by answer extraction rules rather than the actual capability of the model under test. The paper notes that FLARE and the lm-evaluation-harness it forked from use different first-match rules on the same generated outputs, with accuracy differences reaching up to 0.4216, and no leaderboard states which rule set it uses.
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