Do Before You Judge: Self-Reference as a Pathway to Better LLM Evaluation

• URL: http://arxiv.org/abs/2509.19880v1
• Date: Wed, 24 Sep 2025 08:32:45 GMT
• Title: Do Before You Judge: Self-Reference as a Pathway to Better LLM Evaluation
• Authors: Wei-Hsiang Lin, Sheng-Lun Wei, Hen-Hsen Huang, Hsin-Hsi Chen,
• Abstract summary: LLM-as-Judge frameworks are increasingly popular for AI evaluation.<n>Research findings on the relationship between models' generation and judgment abilities remain inconsistent.<n>We propose a self-reference-guided evaluation strategy that leverages a model's own answers as references.
• Score: 22.409170147732464
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: LLM-as-Judge frameworks are increasingly popular for AI evaluation, yet research findings on the relationship between models' generation and judgment abilities remain inconsistent. We investigate this relationship through systematic dataset- and instance-level analyses across 11 models and 21 diverse tasks. Despite both capabilities relying on the same underlying knowledge, our analyses reveal they are only weakly correlated, primarily due to LLMs' sensitivity to the responses being judged. To address this, we propose a self-reference-guided evaluation strategy that leverages a model's own answers as references. This approach significantly strengthens the correlation between generation and judgment abilities, offering a practical path to align these skills and providing a reliable proxy for model selection in evaluation tasks.

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