Summary of A Survey on Large Language Model-based Agents For Statistics and Data Science, by Maojun Sun et al.
A Survey on Large Language Model-based Agents for Statistics and Data Science
by Maojun Sun, Ruijian Han, Binyan Jiang, Houduo Qi, Defeng Sun, Yancheng Yuan, Jian Huang
First submitted to arxiv on: 18 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Machine Learning (cs.LG); Other Statistics (stat.OT)
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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 This survey provides an overview of Large Language Model-based (LLM) data agents that have transformed traditional data analysis. The paper highlights LLM’s capabilities in simplifying complex tasks, making them accessible to users without expertise. It explores current trends in designing LLM-based frameworks with essential features like planning, reasoning, and user interface, enabling minimal human intervention. Case studies demonstrate practical applications of various agents in real-world scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Data science agents powered by Large Language Models (LLMs) are changing the way we analyze data. This survey explains what these “data agents” can do and how they’re making complex tasks easier for people who don’t have a background in data analysis. It also talks about the features that make LLM-based frameworks work well, like being able to plan and reason, and having a user-friendly interface. The paper shares examples of how different data agents are used in real-life situations. |
Keywords
» Artificial intelligence » Large language model
