BenchBrowser -- Collecting Evidence for Evaluating Benchmark Validity

• URL: http://arxiv.org/abs/2603.18019v1
• Date: Wed, 25 Feb 2026 04:02:36 GMT
• Title: BenchBrowser -- Collecting Evidence for Evaluating Benchmark Validity
• Abstract summary: High-level metadata is too coarse to convey the granular reality of benchmarks.<n>BenchBrowser surfaces evaluation items relevant to natural language use cases over 20 benchmark suites.<n>It helps quantify a critical gap between practitioner intent and what benchmarks actually test.
• Score: 29.31434780089624
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Do language model benchmarks actually measure what practitioners intend them to ? High-level metadata is too coarse to convey the granular reality of benchmarks: a "poetry" benchmark may never test for haikus, while "instruction-following" benchmarks will often test for an arbitrary mix of skills. This opacity makes verifying alignment with practitioner goals a laborious process, risking an illusion of competence even when models fail on untested facets of user interests. We introduce BenchBrowser, a retriever that surfaces evaluation items relevant to natural language use cases over 20 benchmark suites. Validated by a human study confirming high retrieval precision, BenchBrowser generates evidence to help practitioners diagnose low content validity (narrow coverage of a capability's facets) and low convergent validity (lack of stable rankings when measuring the same capability). BenchBrowser, thus, helps quantify a critical gap between practitioner intent and what benchmarks actually test.

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