Go to ACL ARR 2026 January homepageRPC-Bench: A Fine-grained Benchmark for Research Paper Comprehension
Keywords: Research Paper Comprehension, Multimodal Benchmark, LLM-as-a-Judge
Abstract: Understanding research papers remains challenging for foundation models due to specialized scientific discourse and complex figures and tables, yet existing benchmarks offer limited fine-grained evaluation at scale. To address this gap, we introduce RPC-Bench, a large-scale question-answering benchmark built from review–rebuttal exchanges of high-quality computer science papers, containing 15K human-verified QA pairs. We design a fine-grained taxonomy aligned with the scientific research flow to assess models’ ability to understand and answer why, what, and how questions in scholarly contexts. We also define an elaborate LLM–human interaction annotation framework to support large-scale labeling and quality control. Following the LLM-as-a-Judge paradigm, we develop a scalable framework that evaluates models on correctness-completeness and conciseness, with high agreement to human judgment. Experiments reveal that even the strongest models (GPT-5) achieve only 68.2% correctness-completeness, dropping to 37.46% after conciseness adjustment, highlighting substantial gaps in precise academic paper understanding.
Paper Type: Long
Research Area: Resources and Evaluation
Research Area Keywords: benchmarking, evaluation methodologies, corpus creation
Contribution Types: Data resources
Languages Studied: English
Submission Number: 4702
Loading