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
@article{arxiv.2505.16667,
title = {ELABORATION: A Comprehensive Benchmark on Human-LLM Competitive Programming},
author = {Xinwei Yang and Zhaofeng Liu and Chen Huang and Jiashuai Zhang and Tong Zhang and Yifan Zhang and Wenqiang Lei},
journal= {arXiv preprint arXiv:2505.16667},
year = {2025}
}
Comments
ACL 2025 Main. Our code and dataset are available at https://github.com/SCUNLP/ELABORATION