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
The rapid advancement and deployment of large language models necessitate more granular and accurate evaluation methods to ensure their reliability and foster continued improvement.
Sophisticated evaluation methods are critical for understanding the true capabilities and limitations of AI models, guiding development, and ensuring safe and effective deployment across various applications.
The ability to automatically identify specific model weaknesses and benchmark imbalances allows for targeted improvements in AI development and more robust evaluation strategies.
- · AI researchers
- · AI developers
- · AI safety researchers
- · Overly simplistic AI benchmarks
- · AI models with unaddressed 'gaps'
Improved understanding of LLM capabilities and limitations at a fine-grained level.
Faster iteration and more targeted development of AI models due to identified competency gaps.
More reliable and trustworthy AI systems being deployed in critical applications across industries.
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