GPTree: Towards Explainable Decision-Making via LLM-powered Decision Trees

• URL: http://arxiv.org/abs/2411.08257v1
• Date: Wed, 13 Nov 2024 00:14:09 GMT
• Title: GPTree: Towards Explainable Decision-Making via LLM-powered Decision Trees
• Authors: Sichao Xiong, Yigit Ihlamur, Fuat Alican, Aaron Ontoyin Yin,
• Abstract summary: GPTree is a novel framework combining explainability of decision trees with the advanced reasoning capabilities of LLMs. Our decision tree achieved a 7.8% precision rate for identifying "unicorn" startups at the inception stage of a startup.
• Score: 0.0
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• Abstract: Traditional decision tree algorithms are explainable but struggle with non-linear, high-dimensional data, limiting its applicability in complex decision-making. Neural networks excel at capturing complex patterns but sacrifice explainability in the process. In this work, we present GPTree, a novel framework combining explainability of decision trees with the advanced reasoning capabilities of LLMs. GPTree eliminates the need for feature engineering and prompt chaining, requiring only a task-specific prompt and leveraging a tree-based structure to dynamically split samples. We also introduce an expert-in-the-loop feedback mechanism to further enhance performance by enabling human intervention to refine and rebuild decision paths, emphasizing the harmony between human expertise and machine intelligence. Our decision tree achieved a 7.8% precision rate for identifying "unicorn" startups at the inception stage of a startup, surpassing gpt-4o with few-shot learning as well as the best human decision-makers (3.1% to 5.6%).

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