arXiv:2606.30931v1 Announce Type: cross Abstract: The LLM Jury, a Panel of LLM Evaluators (PoLL) reporting consensus scores, has become a practical alternative to single-judge LLM evaluation, yet its statistical behavior remains poorly understood. We formalize the LLM Jury under the Huber contamination model and show that PoLL incurs unbounded bias under any positive contamination, regardless of jury size, whenever a single judge fails in a biased, LLM-typical way (mode collapse, sycophancy, safety refusal). Framing jury consensus as classical robust mean estimation, we propose RoPoLL (Robust
The proliferation of LLM-based evaluation systems is exposing the inherent biases and vulnerabilities of single-judge or simplistic panel approaches, necessitating more robust methodologies.
Improving the reliability of LLM evaluation is critical for the credible development and deployment of AI agents and large language models across sensitive applications.
The proposed RoPoLL framework introduces a more statistically sound method for LLM jury consensus, moving past naive aggregation susceptible to common LLM failure modes.
- · AI developers
- · AI-powered evaluation platforms
- · Academic researchers
- · Unrobust LLM evaluation methods
- · Applications reliant on flawed AI judges
Widespread adoption of robust evaluation methods like RoPoLL enhances the trustworthiness and performance of AI systems.
Increased confidence in LLM performance enables faster development and deployment cycles for AI agents and other advanced AI applications.
More reliable AI evaluation could accelerate the development of ethical AI by better identifying and mitigating unfair biases or safety issues in LLMs.
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Read at arXiv cs.LG