YESciEval: Robust LLM-as-a-Judge for Scientific Question Answering

• URL: http://arxiv.org/abs/2505.14279v2
• Date: Thu, 29 May 2025 16:45:00 GMT
• Title: YESciEval: Robust LLM-as-a-Judge for Scientific Question Answering
• Authors: Jennifer D'Souza, Hamed Babaei Giglou, Quentin Münch,
• Abstract summary: Large Language Models (LLMs) drive scientific question-answering on modern search engines, yet their evaluation remains underexplored.<n>We introduce YESciEval, an open-source framework that combines fine-grained rubric-based assessment with reinforcement learning to mitigate optimism bias in evaluators.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Large Language Models (LLMs) drive scientific question-answering on modern search engines, yet their evaluation robustness remains underexplored. We introduce YESciEval, an open-source framework that combines fine-grained rubric-based assessment with reinforcement learning to mitigate optimism bias in LLM evaluators. We release multidisciplinary scienceQ&A datasets, including adversarial variants, with evaluation scores from multiple LLMs. Independent of proprietary models and human feedback, our approach enables scalable, cost-free evaluation. By advancing reliable LLM-as-a-judge models, this work supports AI alignment and fosters robust, transparent evaluation essential for scientific inquiry.

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