arXiv:2509.25359v2 Announce Type: replace Abstract: We present a systematic stress-test of geometric metrics for LLM evaluation. Rank-based geometric properties of internal representations have shown promise as reference-free quality signals, but the conditions under which they are reliable remain unclear. We evaluate eight commonly-used metrics: intrinsic-dimensionality estimators, spectral norms, and related quantities across six tester models (0.5-8B) and eight generators on contrasting tasks, separating genuine geometric signal from text-length effects and from what standard text statistic
The rapid development and deployment of LLMs necessitate more robust and reliable evaluation methods to understand their capabilities and limitations beyond superficial performance metrics.
Improved LLM evaluation directly impacts trust, safety, and the effective integration of AI into critical applications, guiding research and development towards more reliable and interpretable models.
The focus is shifting from solely output-based evaluations to understanding the internal representations of LLMs, which could lead to more robust and less susceptible-to-gaming evaluation protocols.
- · AI researchers
- · LLM developers prioritizing reliability
- · AI safety organizations
- · Developers relying on superficial evaluation
- · LLM competitors with less robust internal mechanisms
More accurate and reliable evaluation metrics will accelerate the development of safer and more capable LLMs.
Standardization of these geometric metrics could emerge, becoming a benchmark for LLM quality and interpretability.
A deeper understanding of internal representations might lead to breakthroughs in foundational AI architectures, moving beyond current transformer limitations.
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Read at arXiv cs.CL