Uncovering Competency Gaps in Large Language Models and Their Benchmarks

arXiv:2512.20638v2 Announce Type: replace Abstract: The evaluation of large language models relies heavily on standardized benchmarks. These benchmarks provide useful aggregated metrics, but can obscure (i) particular sub-areas where the models are weak ("model gaps") and (ii) imbalanced coverage in the benchmarks themselves ("benchmark gaps"). To automatically uncover both types of gaps, we propose a simple new method using concept activations from sparse autoencoders, to identify fine-grained gaps on a per-concept basis. The method also benefits from grounding evaluation in the model's inter

Source: arXiv cs.CL — read the full report at the original publisher.

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