This is the recommended reading list for the paper ”A Survey on Large Language Model-based Agents for Statistics and Data Science“
- AUTOMIND: Adaptive Knowledgeable Agent for Automated Data Science (GitHub)
- ML-Agent: Reinforcing LLM Agents for Autonomous Machine Learning Engineering (Github)
- I-MCTS: Enhancing Agentic AutoML via Introspective Monte Carlo Tree Search
- AutoML-Agent: A Multi-Agent LLM Framework for Full-Pipeline AutoML
- AutoM3L: An Automated Multimodal Machine Learning Framework with Large Language Models
- AutoKaggle: A Multi-Agent Framework for Autonomous Data Science Competitions
- ChatGPT as your Personal Data Scientist
- Data Interpreter: An LLM Agent For Data Science
- Data Formulator 2: Iteratively Creating Rich Visualizations with AI
- DS-Agent: Automated Data Science by Empowering Large Language Models with Case-Based Reasoning
- Execution-based Evaluation for Data Science Code Generation Models
- HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face
- InsightPilot: An LLM-Empowered Automated Data Exploration System
- Investigating Interaction Modes and User Agency in Human-LLM Collaboration for Domain-Specific Data Analysis
- Is GPT-4 a Good Data Analyst?
- JarviX: A LLM No-code Platform for Tabular Data Analysis and Optimization
- LAMBDA: A Large Model-Based Data Agent (Github)
- Large Language Models as Data Preprocessors
- Large Language Models for Tabular Data: Progresses and Future Directions
- Large Language Models for Automated Data Science: Introducing CAAFE for Context-Aware Automated Feature Engineering
- LLM-Enhanced Data Management
- MatPlotAgent: Method and Evaluation for LLM-Based Agentic Scientific Data Visualization
- MLCopilot: Unleashing the Power of Large Language Models in Solving Machine Learning Tasks
- OpenAgents: An Open Platform for Language Agents in the Wild
- SEED: Domain-Specific Data Curation With Large Language Models
- SELA: Tree-Search Enhanced LLM Agents for Automated Machine Learning
- Spider2-V: How Far Are Multimodal Agents From Automating Data Science and Engineering Workflows?
- TaskWeaver: A Code-First Agent Framework
- Text2Analysis: A Benchmark of Table Question Answering with Advanced Data Analysis and Unclear Queries
- Training and Evaluating a Jupyter Notebook Data Science Assistant
- WaitGPT: Monitoring and Steering Conversational LLM Agent in Data Analysis with On-the-Fly Code Visualization
- XInsight: eXplainable Data Analysis Through The Lens of Causality
- Benchmarking Data Science Agents
- BLADE: Benchmarking Language Model Agents for Data-Driven Science
- DA-Code: Agent Data Science Code Generation Benchmark for Large Language Models
- DS-1000: A Natural and Reliable Benchmark for Data Science Code Generation
- InfiAgent-DABench: Evaluating Agents on Data Analysis Tasks
- Table Meets LLM: Can Large Language Models Understand Structured Table Data? A Benchmark and Empirical Study
- Data Analysis in the Era of Generative AI
- Measuring Data Science Automation: A Survey of Evaluation Tools for AI Assistants and Agents
- Large Language Models for Data Science: A Survey
Welcome to submit additions
If you find our work useful in your research, consider citing our paper by:
@article{sun2025survey,
title={A survey on large language model-based agents for statistics and data science},
author={Sun, Maojun and Han, Ruijian and Jiang, Binyan and Qi, Houduo and Sun, Defeng and Yuan, Yancheng and Huang, Jian},
journal={The American Statistician},
pages={1--14},
year={2025},
publisher={Taylor \& Francis}
}
@article{sun2025lambda,
title={Lambda: A large model based data agent},
author={Sun, Maojun and Han, Ruijian and Jiang, Binyan and Qi, Houduo and Sun, Defeng and Yuan, Yancheng and Huang, Jian},
journal={Journal of the American Statistical Association},
pages={1--13},
year={2025},
publisher={Taylor \& Francis}
}