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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&amp;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.</abstract>
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%0 Conference Proceedings
%T YESciEval: Robust LLM-as-a-Judge for Scientific Question Answering
%A D’Souza, Jennifer
%A Babaei Giglou, Hamed
%A Münch, Quentin
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F dsouza-etal-2025-yescieval
%X 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.
%R 10.18653/v1/2025.acl-long.675
%U https://aclanthology.org/2025.acl-long.675/
%U https://doi.org/10.18653/v1/2025.acl-long.675
%P 13749-13783

Markdown (Informal)

[YESciEval: Robust LLM-as-a-Judge for Scientific Question Answering](https://aclanthology.org/2025.acl-long.675/) (D’Souza et al., ACL 2025)

• YESciEval: Robust LLM-as-a-Judge for Scientific Question Answering (D’Souza et al., ACL 2025)

ACL

• Jennifer D’Souza, Hamed Babaei Giglou, and Quentin Münch. 2025. YESciEval: Robust LLM-as-a-Judge for Scientific Question Answering. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 13749–13783, Vienna, Austria. Association for Computational Linguistics.