@inproceedings{chen-etal-2026-rpc,
title = "{RPC}-Bench: A Fine-grained Benchmark for Research Paper Comprehension",
author = "Chen, Yelin and
Zhang, Fanjin and
Sun, Suping and
Pang, Yunhe and
Wang, Yuanchun and
Song, Jian and
Li, XiaoYan and
Hou, Lei and
Zhao, Shu and
Tang, Jie and
Li, Juanzi",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.1277/",
pages = "27683--27717",
ISBN = "979-8-89176-390-6",
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."
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<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.</abstract>
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%0 Conference Proceedings
%T RPC-Bench: A Fine-grained Benchmark for Research Paper Comprehension
%A Chen, Yelin
%A Zhang, Fanjin
%A Sun, Suping
%A Pang, Yunhe
%A Wang, Yuanchun
%A Song, Jian
%A Li, XiaoYan
%A Hou, Lei
%A Zhao, Shu
%A Tang, Jie
%A Li, Juanzi
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F chen-etal-2026-rpc
%X 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.
%U https://aclanthology.org/2026.acl-long.1277/
%P 27683-27717
Markdown (Informal)
[RPC-Bench: A Fine-grained Benchmark for Research Paper Comprehension](https://aclanthology.org/2026.acl-long.1277/) (Chen et al., ACL 2026)
ACL
- Yelin Chen, Fanjin Zhang, Suping Sun, Yunhe Pang, Yuanchun Wang, Jian Song, XiaoYan Li, Lei Hou, Shu Zhao, Jie Tang, and Juanzi Li. 2026. RPC-Bench: A Fine-grained Benchmark for Research Paper Comprehension. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 27683–27717, San Diego, California, United States. Association for Computational Linguistics.