From Uncertain Judgments to Calibrated Rankings: Conformal Elo Estimation for LLM Evaluation
arXiv:2606.13221v2 Announce Type: replace Abstract: Evaluating new large language models typically requires costly human annotation campaigns at scale. LLM-as-a-judge offers a cheaper alternative, but judge scores carry systematic errors - such as position bias, self-preference, or intransitivity - that can strongly miscalibrate the resulting rankings. We quantify the resulting judge-human disagreement at two complementary levels. At the local level, we estimate per-battle uncertainty from the judge's own score differences by propagating calibrated win probabilities rather than hard labels int
The rapid development and deployment of LLMs necessitate more robust and cost-effective evaluation methods, driving innovation in this area.
Accurate and scalable evaluation of LLMs is critical for their improvement and widespread adoption, directly impacting the quality and trustworthiness of AI applications.
The ability to accurately evaluate LLMs without extensive human annotation could significantly accelerate their development cycles and refine their capabilities more efficiently.
- · LLM developers
- · AI evaluation platforms
- · Companies using LLMs
- · Human annotation services (for basic evaluations)
- · LLMs with systematic biases
More reliable and faster iteration on LLM capabilities.
Increased competition among LLMs as evaluation becomes more standardized and accessible.
Deeper integration of LLMs into critical applications due to higher confidence in their performance and fairness.
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Read at arXiv cs.LG