arXiv:2605.30803v1 Announce Type: new Abstract: LLM judges are increasingly used to evaluate open-ended responses, but their scores depend strongly on the rubrics that condition them. A vague rubric asking for a response to be ``helpful and factual'' can reward polished answers that invent facts or violate user intent. We treat reusable rubrics as measurement specifications: changing the rubric changes the response quality measurement induced by a fixed judge. We introduce PReMISE, a framework that, given pairwise human-preference data, (i) discovers a policy-level rubric set, and (ii) audits
The proliferation of LLM judges in evaluating open-ended responses necessitates more robust and reliable evaluation methodologies, as current rubrics are often vague and can lead to flawed assessments.
Improving the reliability and validity of LLM judges through better rubric design is crucial for the effective deployment and trust in AI systems that rely on such evaluations.
The proposed PReMISE framework offers a structured, data-driven approach to discover and audit policy-level rubrics, potentially standardizing LLM evaluation and enhancing fair assessment.
- · AI developers
- · Evaluators of open-ended AI responses
- · Users of AI systems requiring objective assessment
- · Developers relying on vague evaluation metrics
- · AI systems achieving high scores through 'gaming' system biases
More accurate and consistent evaluation of LLM performance and alignment with human intent.
Accelerated development of more sophisticated and ethically aligned LLMs due to clearer feedback mechanisms.
Enhanced trust in AI systems' ability to handle complex, nuanced tasks, leading to wider adoption in critical applications.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI