GPTree: Towards Explainable Decision-Making via LLM-powered Decision Trees (2411.08257v1)

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%).

• The paper introduces GPTree, a novel framework that integrates LLM reasoning with decision trees to enhance interpretability without extensive feature engineering.
• It demonstrates superior precision by achieving 7.8% success in identifying unicorn startups and up to 17.9% with expert feedback in the VC domain.
• The framework empowers expert-in-the-loop intervention, ensuring scalable, adaptable, and transparent decision-making in high-stakes environments.

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

The research paper titled "GPTree: Towards Explainable Decision-Making via LLM-powered Decision Trees" introduces a novel framework that marries the interpretability of decision trees with the reasoning prowess of LLMs. The work primarily addresses a gap in decision-making processes where traditional decision trees, despite their clarity, falter in handling non-linear, high-dimensional data, contrasting with neural networks which excel in such environments at the cost of explainability.

Framework Overview

GPTree operates by dynamically splitting data samples using LLMs to incorporate advanced reasoning into decision trees while maintaining tree-based model transparency. One of the standout features of GPTree is its elimination of extensive feature engineering and prompt chaining complexities. Instead, it uses a task-specific prompt to aggregate insights, avoiding the cumbersome nature of human-led prompt generation typically needed in traditional LLM applications. Additionally, the authors introduce an "expert-in-the-loop" system that allows human agents to intervene, optimize, and refine decision paths, therefore highlighting a symbiotic relationship between machine intelligence and human expertise.

Experimental Results

The empirical evaluation, as detailed in the paper, is conducted within the Venture Capital (VC) landscape—a domain reliant on explainable decision-making. Here, GPTree demonstrates its practicality by outperforming traditional benchmarks, achieving a precision of 7.8% in identifying potential unicorn startups, which exceeds the performance of models like gpt-4o in few-shot learning contexts as well as expert human decision-makers' precision rates ranging from 3.1% to 5.6%.

Through a rigorous cross-validation process, the paper presents an optimized decision-making framework which is both scalable and adaptable to incoming advancements in LLMs. Moreover, proprietary models enhanced with expert feedback achieved even higher precision rates up to 17.9%, showcasing the potential for deployment in high-stakes environments.

Theoretical and Practical Implications

Theoretically, this paper is significant as it proposes a paradigm shift in integrating LLMs with decision tree structures to solve a fundamental trade-off between explainability and predictive capability. It propounds the utility of coded and clustered nodes, alongside inference-based nodes, to manage diverse data types while maintaining model interpretability. This framework supports scalability as newer LLM architectures are integrated to boost performance metrics further without foundational changes to the tree structure.

Practically, GPTree's design showcases efficiency in handling text-rich, multi-modal datasets, making it versatile across sectors needing rapid, accurate, and explainable decisions, such as finance, healthcare, and beyond. It also sets a new standard by effectively harnessing expert feedback post-model training, which is proven to enhance accuracy in the paper's experiments.

Speculation on Future Developments

As more sophisticated LLMs emerge, their application within the GPTree framework could further enhance the efficacy across varied domains, including more complex multi-modal tasks. Future developments could see wider incorporation in sectors where regulatory demands for explainable AI are critical, such as in compliance-heavy industries.

In conclusion, the GPTree framework represents a notable advancement towards combining decision-making transparency with the robust reasoning capabilities of LLMs. This enables significant improvements over traditional methods while opening doors for AI applications in environments where interpretability is as essential as accuracy.