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arXiv · 2505.14279

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

Jennifer D'Souza·Hamed Babaei Giglou·Quentin Münch
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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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Jennifer D'Souza, Hamed Babaei Giglou, Quentin Münch. 2025-05-20. YESciEval: Robust LLM-as-a-Judge for Scientific Question Answering. https://arxiv.org/abs/2505.14279

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