Learning to Judge: LLMs Designing and Applying Evaluation Rubrics

• URL: http://arxiv.org/abs/2602.08672v1
• Date: Mon, 09 Feb 2026 13:56:06 GMT
• Title: Learning to Judge: LLMs Designing and Applying Evaluation Rubrics
• Authors: Clemencia Siro, Pourya Aliannejadi, Mohammad Aliannejadi,
• Abstract summary: Large language models (LLMs) are increasingly used as evaluators for natural language generation.<n>We introduce GER-Eval to investigate whether LLMs can design and apply their own evaluation rubrics.
• Score: 18.936553687978087
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Large language models (LLMs) are increasingly used as evaluators for natural language generation, applying human-defined rubrics to assess system outputs. However, human rubrics are often static and misaligned with how models internally represent language quality. We introduce GER-Eval (Generating Evaluation Rubrics for Evaluation) to investigate whether LLMs can design and apply their own evaluation rubrics. We evaluate the semantic coherence and scoring reliability of LLM-defined criteria and their alignment with human criteria. LLMs reliably generate interpretable and task-aware evaluation dimensions and apply them consistently within models, but their scoring reliability degrades in factual and knowledge-intensive settings. Closed-source models such as GPT-4o achieve higher agreement and cross-model generalization than open-weight models such as Llama. Our findings position evaluation as a learned linguistic capability of LLMs, consistent within models but fragmented across them, and call for new methods that jointly model human and LLM evaluative language to improve reliability and interpretability.

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