# YESciEval: Robust LLM-as-a-Judge for Scientific Question Answering Source: [YESciEval: Robust LLM-as-a-Judge for Scientific Question Answering]() Collection index: [llms.txt]() Data updated: 2025-12-25T21:44:29.398Z Authors: Jennifer D'Souza, Hamed Babaei Giglou, Quentin M\\"unch Published: 2025-05-20T12:30:46.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]() [Implementation]()