LLM evaluation rules: Ensure factual evidence and avoid averaging scores

This article details three rules for ensuring Large Language Models (LLMs) do not fabricate justifications when acting as evaluators. The core principle is that any score assigned must be directly supported by exact textual evidence from the source material, preventing the LLM from generating convincing but baseless rationales. The rules emphasize that if contradictory evidence exists, the score must be lowered, and the LLM is prevented from averaging scores across a conversation, ensuring that a single negative instance dictates the evaluation. AI

IMPACT Provides a method to improve LLM reliability and reduce "hallucinations" in evaluative tasks.

RANK_REASON The article describes a method for improving LLM output quality, which is a tool or technique.

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