Update README

README.md

@@ -174,4 +174,128 @@ configs:
   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 sourced from platforms released after October 2023 to mitigate data contamination, 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
+- **Recent competitive programming problems** (post-October 2023) to avoid data contamination
+- **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, with release dates after October 2023 to minimize data contamination.
+
+## 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 released after October 2023 to minimize data contamination. 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).