AI Research
PReMISE: Policy Rubrics as Measurement Specifications for LLM Judges
A new framework for formalizing and stress-testing the rubric-based evaluation of large language models.
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 trea...
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ArXiv CS.AI
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ArXiv CS.AI
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PReMISE addresses the critical fragility of LLM judges by treating rubrics as formal measurement specifications rather than mere suggestions. This framework provides a standardized way to audit and repair evaluation criteria, significantly reducing the gap between model scores and genuine human preference.
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