Correcting Prompt Dependence in LLM Benchmarks: A Bayesian Hierarchical Model with Embedding-Space Clustering
arXiv:2510.05709v2 Announce Type: replace-cross Abstract: LLM benchmarking metrics often misstate performance and uncertainty as they rely on two assumptions that frequently do not hold in practice: (i) a sufficient number of evaluations are available for classical inference, and (ii) test prompts are independent. We propose a corrective Bayesian hierarchical model with embedding-space clustering that provides robust performance metrics in limited-data settings while correcting for prompt dependence. We apply the approach to adversarial robustness benchmarks, showing consistent recovery of clu
The proliferation of LLM applications and the increasing reliance on their benchmarks necessitate more robust evaluation methodologies to ensure reliable performance assessment.
This development addresses a fundamental flaw in how LLMs are evaluated, potentially leading to more accurate performance metrics and better-informed deployment decisions.
LLM benchmarking can now account for prompt dependence and limited data, providing more reliable assessments of model capabilities and adversarial robustness.
- · LLM developers
- · AI researchers
- · Enterprises deploying LLMs
- · Responsible AI initiatives
- · Benchmarking methodologies relying on naive assumptions
- · LLMs with inflated performance claims
More accurate understanding of true LLM performance and limitations.
Improved development cycles for LLMs as benchmarks provide truer signals for progress.
Enhanced trust in AI systems due to more rigorous and transparent evaluation practices.
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Read at arXiv cs.CL