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--- |
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license: mit |
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task_categories: |
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- table-question-answering |
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- text-generation |
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- summarization |
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language: |
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- en |
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pretty_name: DA-Code |
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size_categories: |
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- 1B<n<10B |
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tags: |
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- code |
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configs: |
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- config_name: default |
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data_files: |
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- split: test |
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path: "test.csv" |
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sep: "," |
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--- |
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# [EMNLP2024] DA-Code: Agent Data Science Code Generation Benchmark for Large Language Models |
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DA-Code is a comprehensive evaluation dataset designed to assess the data analysis and code generation capabilities of LLM in agent-based data science tasks. Our papers and experiment reports have been published on Arxiv. |
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## Dataset Overview |
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- 500 complex real-world data analysis tasks across Data Wrangling (DW), Machine Learning (ML), and Exploratory Data Analysis (EDA). |
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- Tasks cover the entire data analysis pipeline, from raw data handling to gaining insights using SQL and Python. |
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- Each example is meticulously designed to ensure high complexity and quality, with robust evaluation suites. |
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- An interactive sandbox environment allows LLMs/Agents to autonomously explore, reason, and complete tasks. |
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## Usage |
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This dataset can be used to: |
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- Evaluate LLMs’ data analysis and code generation capabilities |
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- Benchmark autonomous reasoning in real-world tasks |
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- Develop and test multi-step data analysis strategies |
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## Citation |
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If you use this dataset in your research, please cite our paper: |
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``` |
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``` |
