arXiv:2509.22888v2 Announce Type: replace-cross Abstract: Standard LLM evaluation practices compress diverse abilities into single scores, obscuring their inherently multidimensional nature. We present JE-IRT, a geometric item-response framework that embeds both LLMs and questions in a shared space. For question embeddings, the direction encodes semantics and the norm encodes difficulty, while correctness on each question is determined by the geometric interaction between the model and question embeddings. This geometry replaces a global ranking of LLMs with topical specialization and enables
The proliferation of LLMs and increasing complexity of AI systems necessitate more nuanced evaluation methods beyond simplistic benchmarks.
This framework offers a more sophisticated understanding of LLM capabilities, moving beyond single-score metrics to diagnose specific strengths and weaknesses.
LLM evaluation could shift from global rankings to a multidimensional assessment, enabling better model selection and targeted development for specific tasks.
- · AI researchers
- · LLM developers
- · AI product managers
- · Simplistic LLM benchmark creators
- · General-purpose LLMs without clear specializations
Improved understanding of LLM 'intelligence' and where different models excel.
More efficient and targeted training of LLMs by identifying areas for improvement based on geometric evaluation.
The development of 'specialist LLMs' tailored for very specific tasks rather than aiming for general, undifferentiated performance.
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Read at arXiv cs.CL