Are Benchmarks Reliable? Toward Structural Diagnosis via Sample-Level Capability Boundaries

Title:Are Benchmarks Reliable? Toward Structural Diagnosis via Sample-Level Capability Boundaries

Abstract:Evaluating large language models (LLMs) relies heavily on benchmark scores, yet aggregate metrics can obscure whether benchmark samples reliably support model comparison. We introduce \textbf{BSDProbe}, a sample-level framework for \emph{benchmark structural diagnosis} that estimates capability boundaries from repeated-response trajectories along ordered model axes. BSDProbe summarizes samples by boundary position, boundary width, boundary-signal validity, and order consistency, then aggregates them into benchmark-level structural profiles. Experiments on six benchmarks show that benchmark reliability is axis-conditioned and heterogeneous: GSM8K and MATH exhibit the most stable measurement structures, MMLU and TriviaQA are relatively stable but heterogeneous, while GPQA and PopQA show stronger axis-conditioned risks. These profiles remain consistent across Qwen3, Qwen2.5, and cross-model axes. BSDProbe further selects compact high-value subsets whose model discriminability reaches up to $8.58\times$ that of the full benchmark. These results suggest that reliable benchmark use requires examining sample-level capability boundaries beyond leaderboard scores.

Artificial Intelligence (cs.AI)

arXiv:2609.33196 [cs.AI]

(or arXiv:2609.33196v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2609.33196 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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