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SkillEval framework offers detailed LLM ability profiles beyond scalar scores

By PulseAugur Editorial · [1 sources] ·

A new framework called SkillEval has been developed to provide a more nuanced evaluation of large language models (LLMs) beyond single scalar scores. SkillEval leverages LLMs' meta-cognitive abilities to identify interpretable skills and applies cognitive diagnosis models from psychometrics to assess per-skill abilities. Experiments on thousands of LLMs across multiple benchmarks demonstrate that SkillEval generates more reliable and informative model profiles, which can predict performance on unseen items and reduce inference costs by over 60% through dynamic model routing. AI

IMPACT Provides a more granular understanding of LLM capabilities, potentially optimizing model routing and reducing inference costs.

RANK_REASON The item describes a new research paper introducing a novel evaluation framework for LLMs. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

SkillEval framework offers detailed LLM ability profiles beyond scalar scores

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The item describes a new research paper introducing a novel evaluation framework for LLMs. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Berke Arda, Peng Cui, Qiaoyuan Zheng, Rudolf Debelak, Mubashara Akhtar, Mrinmaya Sachan ·

    SkillEval: Learning Interpretable Ability Profiles of LLMs via Cognitive Diagnosis Models

    arXiv:2610.28767v1 Announce Type: new Abstract: Current evaluation of LLMs on benchmarks typically aggregates performance over individual examples into a single scalar score. This approach is overly coarse and ignores the diverse scope of skills covered by benchmark datasets. To …

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