ELABORATION: A Comprehensive Benchmark on Human-LLM Competitive Programming
This work addresses the problem of fragmented studies in human-LLM collaboration for competitive programming, providing a foundational tool for researchers, but it is incremental as it builds on existing empirical methods.
The paper tackles the lack of a comprehensive understanding of human-LLM collaboration in competitive programming by introducing ELABORATION, a benchmark that includes a taxonomy of human feedback, a dataset for collaboration, and an assessment framework, identifying strengths and weaknesses of existing methods.
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