Summary of Ds-agent: Automated Data Science by Empowering Large Language Models with Case-based Reasoning, By Siyuan Guo et al.
DS-Agent: Automated Data Science by Empowering Large Language Models with Case-Based Reasoning
by Siyuan Guo, Cheng Deng, Ying Wen, Hechang Chen, Yi Chang, Jun Wang
First submitted to arxiv on: 27 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 The authors propose DS-Agent, a novel framework that leverages large language models (LLMs) and case-based reasoning (CBR) to automate data science tasks. The goal is to comprehend task requirements and build the best-fit machine learning models. Existing LLM agents are limited by generating unreasonable experiment plans, which DS-Agent aims to address. The framework follows a CBR structure for automatic iteration, capitalizing on expert knowledge from Kaggle and facilitating performance improvement through feedback mechanisms. Empirically, DS-Agent achieves 100% success rate with GPT-4 in the development stage and improves average one-pass rates across alternative LLMs in the deployment stage. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary DS-Agent is a new way to help machines do data science tasks better. The goal is to make computers understand what they need to do, then find the best machine learning models to solve the problem. Right now, computers can’t come up with good plans for experiments, so DS-Agent helps by using a special technique called case-based reasoning. This allows the computer to learn from past successes and apply them to new problems. The authors tested DS-Agent and found that it works well, especially when using a powerful language model like GPT-4. |
Keywords
* Artificial intelligence * Gpt * Language model * Machine learning
