Summary of Data Interpreter: An Llm Agent For Data Science, by Sirui Hong et al.
Data Interpreter: An LLM Agent For Data Science
by Sirui Hong, Yizhang Lin, Bang Liu, Bangbang Liu, Binhao Wu, Ceyao Zhang, Chenxing Wei, Danyang Li, Jiaqi Chen, Jiayi Zhang, Jinlin Wang, Li Zhang, Lingyao Zhang, Min Yang, Mingchen Zhuge, Taicheng Guo, Tuo Zhou, Wei Tao, Xiangru Tang, Xiangtao Lu, Xiawu Zheng, Xinbing Liang, Yaying Fei, Yuheng Cheng, Zhibin Gou, Zongze Xu, Chenglin Wu
First submitted to arxiv on: 28 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Data Interpreter, an LLM-based agent, addresses the challenges of solving long-term interconnected tasks in data science scenarios. Previous approaches typically focus on individual tasks, making it difficult to assess the complete workflow. To overcome this limitation, Data Interpreter incorporates two key modules: Hierarchical Graph Modeling and Programmable Node Generation. The former breaks down complex problems into manageable subproblems, enabling dynamic node generation and graph optimization, while the latter refines and verifies each subproblem to iteratively improve code generation results and robustness. Experimental results demonstrate the superiority of Data Interpreter on various benchmarks, including InfiAgent-DABench, MATH dataset, and machine learning tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Data Interpreter is a new AI agent that helps solve complex data science problems. It’s like a super-smart researcher that can break down big tasks into smaller ones, make adjustments as needed, and even learn from its mistakes. The agent uses two main tools: one to divide the problem into manageable parts, and another to check and improve each part before moving on. By combining these tools, Data Interpreter can solve problems more accurately than other agents. It’s been tested on several challenges and has shown significant improvements over current methods. |
Keywords
* Artificial intelligence * Machine learning * Optimization
