arXiv:2601.21817v2 Announce Type: replace-cross Abstract: Evaluating large language models (LLMs) on open-ended tasks without ground-truth labels is increasingly done via the LLM-as-a-judge paradigm. A critical but under-modeled issue is that judge LLMs differ substantially in reliability; treating all judges equally can yield biased leaderboards and misleading uncertainty estimates. More data can make evaluation more confidently wrong under misspecified aggregation. We propose a judge-aware ranking framework that extends the Bradley-Terry-Luce model by introducing judge-specific discriminatio
The proliferation of increasingly capable LLMs necessitates more rigorous evaluation methods, especially as their applications expand into open-ended tasks.
Reliable evaluation of LLMs is critical for their responsible deployment, preventing biases, and ensuring that development efforts are directed effectively.
The ability to more accurately evaluate and compare LLMs without ground truth, accounting for the variability in judging capabilities, will refine model development and selection.
- · AI researchers
- · LLM developers
- · Organizations deploying LLMs
- · Unreliable LLM evaluation methods
- · LLMs with inflated performance claims
Improved methods for evaluating large language models.
More trustworthy benchmarks and leaderboards for LLMs, leading to better model selection.
Accelerated development of robust and fair LLMs, as evaluation becomes a more reliable guide for progress.
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Read at arXiv cs.LG