arXiv:2507.20208v2 Announce Type: replace Abstract: Current evaluations of large language models (LLMs) rely heavily on a growing collection of benchmarks and on aggregate benchmark scores, yet it remains unclear what this comparison actually captures, and what these scores reveal about models' underlying capabilities. Here, we propose a new paradigm for LLM evaluation, by asking whether benchmark performance reflects many independent abilities, or rather relies on a small number of shared dimensions. To answer this, we apply Factor Analysis (FA) to a massive performance matrix of LLMs versus
The proliferation of Large Language Models (LLMs) and the increasing complexity of their evaluation necessitate more robust and insightful metrics beyond aggregate benchmarks.
A refined understanding of LLM capabilities through factor analysis will allow for more targeted development and application, moving beyond opaque aggregate scores to actionable insights into underlying 'skills'.
Current LLM evaluation methods, heavily reliant on aggregate benchmark scores, will be supplemented or potentially replaced by skill-based factor analysis, offering a clearer picture of model strengths and weaknesses.
- · AI Researchers
- · LLM Developers
- · Organizations deploying LLMs
- · Benchmarks focused solely on aggregate scores
- · LLMs with broad-but-shallow capabilities
The adoption of factor analysis leads to more precise identification of LLM skill gaps and proficiencies.
This precision drives the development of more specialized and efficient LLMs tailored for specific tasks, moving away from general-purpose models.
A clearer understanding of LLM capabilities accelerates their integration into complex workflows and potentially impacts the development trajectory of AI agents, making their deployment more targeted and effective.
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