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arXiv · 2505.16667

ELABORATION: A Comprehensive Benchmark on Human-LLM Competitive Programming

Xinwei Yang·Zhaofeng Liu·Chen Huang·Jiashuai Zhang·Tong Zhang·Yifan Zhang·Wenqiang Lei
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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 costeffective 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 future improvement. Our code and dataset are available at https://github.com/SCUNLP/ELABORATION

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Xinwei Yang, Zhaofeng Liu, Chen Huang, Jiashuai Zhang, Tong Zhang, Yifan Zhang, Wenqiang Lei. 2025-05-22. ELABORATION: A Comprehensive Benchmark on Human-LLM Competitive Programming. https://arxiv.org/abs/2505.16667

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