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

arXiv: https://arxiv.org/abs/2411.08257 · HTML: https://mlsanity.com/paper/2411.08257/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Nov 2024.

## Built on by (most cited first, 13 of 13)

- [Policy Induction: Predicting Startup Success via Explainable Memory-Augmented In-Context Learning](https://mlsanity.com/paper/2505.21427/) (2025)
- [Reasoning-Based AI for Startup Evaluation (R.A.I.S.E.): A Memory-Augmented, Multi-Step Decision Framework](https://mlsanity.com/paper/2504.12090/) (2025)
- [Ensembling LLM-Induced Decision Trees for Explainable and Robust Error Detection](https://mlsanity.com/paper/2512.07246/) (2025)
- [DIALECTIC: A Multi-Agent System for Startup Evaluation](https://mlsanity.com/paper/2603.12274/) (2026)
- [Random Rule Forest (RRF): Interpretable and Manageable Ensembles of LLM-Generated Questions for Predicting Success from Unstructured Data](https://mlsanity.com/paper/2505.24622/) (2025)
- [LLM-AR: LLM-powered Automated Reasoning Framework](https://mlsanity.com/paper/2510.22034/) (2025)
- [Democratic ICAI: Debating Our Way to Steering Principles from Preferences](https://mlsanity.com/paper/2606.28294/) (2026)
- [From Limited Data to Rare-event Prediction: LLM-powered Feature Engineering and Multi-model Learning in Venture Capital](https://mlsanity.com/paper/2509.08140/) (2025)
- [From Stochastic Answers to Verifiable Reasoning: Interpretable Decision-Making with LLM-Generated Code](https://mlsanity.com/paper/2603.13287/) (2026)
- [CoFEE: Reasoning Control for LLM-Based Feature Discovery](https://mlsanity.com/paper/2604.21584/) (2026)
- [Distilling LLM Reasoning into an Interpretable Policy Tree for Human-AI Collaboration](https://mlsanity.com/paper/2606.08596/) (2026)
- [Natural-Language Policies to Executable Decisions: An Interpretable Large Language Model Framework](https://mlsanity.com/paper/2608.26124/) (2026)
- [From Prompts to Trees: Effective LLM-Guided Tree Generation for Few-Shot Tabular Classification](https://mlsanity.com/paper/2610.10227/) (2026)

## Abstract (arXiv)

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%).