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    <abstract>While recent research increasingly emphasizes the value of human-LLM collaboration in competitive programming and proposes numerous empirical methods, a comprehensive understanding remains elusive due to the fragmented nature of existing studies and their use of diverse, application-specific human feedback. Thus, our work serves a three-fold purpose: First, we present the first taxonomy of human feedback consolidating the entire programming process, which promotes fine-grained evaluation. Second, we introduce ELABORATIONSET, a novel programming dataset specifically designed for human-LLM collaboration, meticulously annotated to enable large-scale simulated human feedback and facilitate cost-effective real human interaction studies. Third, we introduce ELABORATION, a novel benchmark to facilitate a thorough assessment of human-LLM competitive programming. With ELABORATION, we pinpoint strengthes and weaknesses of existing methods, thereby setting the foundation for furture improvement. Our dataset and code will be openly released.</abstract>
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%0 Conference Proceedings
%T ELABORATION: A Comprehensive Benchmark on Human-LLM Competitive Programming
%A Yang, Xinwei
%A Liu, Zhaofeng
%A Huang, Chen
%A Zhang, Jiashuai
%A Zhang, Tong
%A Zhang, Yifan
%A Lei, Wenqiang
%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 yang-etal-2025-elaboration
%X While recent research increasingly emphasizes the value of human-LLM collaboration in competitive programming and proposes numerous empirical methods, a comprehensive understanding remains elusive due to the fragmented nature of existing studies and their use of diverse, application-specific human feedback. Thus, our work serves a three-fold purpose: First, we present the first taxonomy of human feedback consolidating the entire programming process, which promotes fine-grained evaluation. Second, we introduce ELABORATIONSET, a novel programming dataset specifically designed for human-LLM collaboration, meticulously annotated to enable large-scale simulated human feedback and facilitate cost-effective real human interaction studies. Third, we introduce ELABORATION, a novel benchmark to facilitate a thorough assessment of human-LLM competitive programming. With ELABORATION, we pinpoint strengthes and weaknesses of existing methods, thereby setting the foundation for furture improvement. Our dataset and code will be openly released.
%R 10.18653/v1/2025.acl-long.4
%U https://aclanthology.org/2025.acl-long.4/
%U https://doi.org/10.18653/v1/2025.acl-long.4
%P 59-104

Markdown (Informal)

[ELABORATION: A Comprehensive Benchmark on Human-LLM Competitive Programming](https://aclanthology.org/2025.acl-long.4/) (Yang et al., ACL 2025)

• ELABORATION: A Comprehensive Benchmark on Human-LLM Competitive Programming (Yang et al., ACL 2025)

ACL

• Xinwei Yang, Zhaofeng Liu, Chen Huang, Jiashuai Zhang, Tong Zhang, Yifan Zhang, and Wenqiang Lei. 2025. ELABORATION: A Comprehensive Benchmark on Human-LLM Competitive Programming. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 59–104, Vienna, Austria. Association for Computational Linguistics.