--- dataset_info: features: - name: id dtype: string - name: title dtype: string - name: title_slug dtype: string - name: description dtype: string - name: description_md dtype: string - name: difficulty dtype: string - name: tags list: string - name: source dtype: string - name: url dtype: string - name: type dtype: string - name: release_timestamp dtype: int64 - name: release_date dtype: string - name: time_limit_nanos dtype: int64 - name: memory_limit_bytes dtype: int64 - name: starter_code struct: - name: c dtype: string - name: cpp dtype: string - name: csharp dtype: string - name: dart dtype: string - name: elixir dtype: string - name: erlang dtype: string - name: golang dtype: string - name: java dtype: string - name: javascript dtype: string - name: kotlin dtype: string - name: php dtype: string - name: python dtype: string - name: python3 dtype: string - name: racket dtype: string - name: ruby dtype: string - name: rust dtype: string - name: scala dtype: string - name: swift dtype: string - name: typescript dtype: string - name: solutions struct: - name: cpp struct: - name: code dtype: string - name: memory dtype: int64 - name: memoryDistribution dtype: string - name: runtime dtype: int64 - name: runtimeDistribution dtype: string - name: golang struct: - name: code dtype: string - name: memory dtype: int64 - name: memoryDistribution dtype: string - name: runtime dtype: int64 - name: runtimeDistribution dtype: string - name: java struct: - name: code dtype: string - name: memory dtype: int64 - name: memoryDistribution dtype: string - name: runtime dtype: int64 - name: runtimeDistribution dtype: string - name: javascript struct: - name: code dtype: string - name: memory dtype: int64 - name: memoryDistribution dtype: string - name: runtime dtype: int64 - name: runtimeDistribution dtype: string - name: python3 struct: - name: code dtype: string - name: memory dtype: int64 - name: memoryDistribution dtype: string - name: runtime dtype: int64 - name: runtimeDistribution dtype: string - name: ruby struct: - name: code dtype: string - name: memory dtype: int64 - name: memoryDistribution dtype: string - name: runtime dtype: int64 - name: runtimeDistribution dtype: string - name: test_case_generator dtype: string - name: evaluator dtype: string - name: generated_tests dtype: string - name: test_runners struct: - name: cpp dtype: string - name: golang dtype: string - name: java dtype: string - name: javascript dtype: string - name: python3 dtype: string - name: ruby dtype: string splits: - name: test num_bytes: 3865548641 num_examples: 623 download_size: 2341977516 dataset_size: 3865548641 configs: - config_name: default data_files: - split: test path: data/test-* license: apache-2.0 task_categories: - question-answering language: - en tags: - code pretty_name: EffiBench-X size_categories: - n<1K --- # Dataset Card for EffiBench-X **EffiBench-X** is the first multi-language benchmark designed specifically to evaluate the efficiency of LLM-generated code across six programming languages: Python, C++, Java, JavaScript, Ruby, and Golang. The dataset comprises 623 competitive programming problems paired with human expert solutions as efficiency baselines. ## Dataset Details ### Dataset Description EffiBench-X addresses critical limitations in existing code generation benchmarks by providing: - **Multi-language evaluation** across Python, C++, Java, JavaScript, Ruby, and Golang - **Efficiency-focused metrics** including execution time, memory peak, and memory integral - **Human expert baselines** for reliable efficiency comparison - **Curated by:** Yuhao Qing, Boyu Zhu, Mingzhe Du, Zhijiang Guo, Terry Yue Zhuo, Qianru Zhang, Jie M. Zhang, Heming Cui, Siu-Ming Yiu, Dong Huang, See-Kiong Ng, Luu Anh Tuan - **Institutions:** HKU, UCL, NTU, NUS, HKUST, Monash University, CSIRO's Data61, KCL - **Language(s) (NLP):** English - **License:** Apache License 2.0 ### Dataset Sources - **Repository:** [EffiBench-X (GitHub)](https://github.com/EffiBench/EffiBench-X) - **Dataset:** [EffiBench/effibench-x](https://huggingface.co/datasets/EffiBench/effibench-x) - **Paper:** [arXiv:2505.13004](https://arxiv.org/abs/2505.13004) - **Problem Sources:** - [LeetCode](https://leetcode.com) - [Aizu Online Judge](https://onlinejudge.u-aizu.ac.jp/) - [AtCoder](https://atcoder.jp) - [CodeChef](https://www.codechef.com) - [Codeforces](https://codeforces.com) ## Uses ### Direct Use - **Benchmarking LLM code generation efficiency**: Evaluate models on runtime performance, memory usage, and correctness across multiple languages - **Cross-language performance analysis**: Compare model capabilities across different programming paradigms - **Model development**: Train and fine-tune models for efficient code generation - **Research**: Study efficiency gaps between LLM-generated and human expert code ### Out-of-Scope Use - **Production deployment without validation**: Solutions should be verified before production use - **Security-critical applications**: The dataset focuses on algorithmic efficiency, not security - **Non-competitive programming domains**: Problems are algorithmic in nature and may not represent all software engineering contexts ## Dataset Structure The dataset contains 623 problems categorized into: - **Functional problems**: Implement specific functions/classes with I/O handled by test templates - **Standard I/O problems**: Complete programs reading from stdin and writing to stdout Key fields per record include: - `id`, `title`, `title_slug`, `description`, `description_md`, `difficulty`, `tags`, `source`, `url`, `type` - Limits: `time_limit_nanos`, `memory_limit_bytes` - Code artifacts: - `starter_code`: language-keyed starter snippets - `solutions`: language-keyed canonical solutions (e.g., for `cpp`, `golang`, `java`, `javascript`, `python3`, `ruby`) - `test_case_generator`: executable code string that programmatically produces tests - `evaluator`: executable code string to determine pass/fail given expected vs. program output - `generated_tests`: serialized tests produced by the generator - `test_runners`: language-keyed runner templates for executing solutions All problems are from competitive programming platforms. ## Dataset Creation ### Curation Rationale Existing code generation benchmarks primarily focus on functional correctness with limited attention to efficiency, often restricted to Python. EffiBench-X addresses three critical limitations: 1. **Language diversity**: Extends beyond Python to include statically-typed (C++, Java, Go) and dynamically-typed languages (Python, JavaScript, Ruby) 2. **Data contamination**: Uses recent problems (post-October 2023) to avoid memorization effects 3. **Complexity**: Features algorithmically challenging problems requiring optimization techniques ### Source Data #### Data Collection and Processing Problems are curated from competitive programming platforms. Each problem includes: - Human expert solutions verified for correctness and efficiency - 100 programmatically generated test cases - Test runners and evaluators for automated assessment - Cross-language validation to ensure consistency #### Who are the source data producers? - **Problem creators**: Competitive programming platforms and contest organizers - **Solution authors**: Human expert programmers from competitive programming communities - **Dataset curators**: EffiBench research team ## Citation Please cite our paper if you use this dataset: ```bibtex @article{qing2025effibench, title={EffiBench-X: A Multi-Language Benchmark for Measuring Efficiency of LLM-Generated Code}, author={Qing, Yuhao and Zhu, Boyu and Du, Mingzhe and Guo, Zhijiang and Zhuo, Terry Yue and Zhang, Qianru and Zhang, Jie M and Cui, Heming and Yiu, Siu-Ming and Huang, Dong and Ng, See-Kiong and Tuan, Luu Anh}, journal={Advances in neural information processing systems}, year={2025} } ``` ## More Information - **Dataset Statistics**: 623 problems, 100 test cases per problem, 6 languages - **Evaluation**: Sandboxed execution environment for consistent performance measurements - For detailed information and benchmark results, please refer to the [paper](https://arxiv.org/abs/2505.13004) and [GitHub repository](https://github.com/EffiBench/EffiBench-X) ## Dataset Card Contact For questions and feedback, please open an issue on our [GitHub repository](https://github.com/EffiBench/EffiBench-X).