Summary of Gptree: Towards Explainable Decision-making Via Llm-powered Decision Trees, by Sichao Xiong et al.
GPTree: Towards Explainable Decision-Making via LLM-powered Decision Trees
by Sichao Xiong, Yigit Ihlamur, Fuat Alican, Aaron Ontoyin Yin
First submitted to arxiv on: 13 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE)
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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 novel framework, GPTree, combines the explainability of traditional decision trees with the advanced reasoning capabilities of Large Language Models (LLMs). This framework eliminates the need for feature engineering and prompt chaining, instead leveraging a tree-based structure to dynamically split samples. The expert-in-the-loop feedback mechanism enables human intervention to refine and rebuild decision paths, emphasizing the harmony between human expertise and machine intelligence. GPTree is applied to identify “unicorn” startups at the inception stage, achieving a 7.8% precision rate, surpassing gpt-4o with few-shot learning as well as the best human decision-makers. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GPTree is a new way for computers to make decisions that’s easy to understand and works well even when dealing with complex data. It combines the strengths of two different approaches: traditional decision trees, which are good at explaining their choices, but struggle with complex data; and neural networks, which can capture complex patterns, but don’t provide clear explanations. GPTree is designed to work with minimal human input, using a special prompt to help it make decisions. It’s also better than other AI models when identifying successful startups. |
Keywords
» Artificial intelligence » Feature engineering » Few shot » Gpt » Precision » Prompt
