YESciEval: Robust LLM-as-a-Judge for Scientific Question Answering
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Abstract (arXiv)
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.
Key facts. Cited by 16 papers in the map (data through 14 Oct 2026). Builds on 55 of its 92 references that are in the map. First posted on arXiv 20 May 2025.
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55 of 92 references.
References · 55
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- 2026On Temperature-Constrained Non-Deterministic Machine Translation: Potential and Evaluation20 Jan 2026
- 2026RPC-Bench: A Fine-grained Benchmark for Research Paper Comprehension14 Jan 2026
- 2026Structure-BiEval: A Self-Supervised, Dual-Track Framework for Decoupling Structure and Content in LLM Evaluation for Web Information Systems9 Jan 2026
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