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%).

翻译：传统决策树算法具有可解释性，但难以处理非线性、高维数据，限制了其在复杂决策场景中的应用。神经网络虽擅长捕捉复杂模式，却在过程中牺牲了可解释性。本研究提出GPTree，一种融合决策树可解释性与大语言模型高级推理能力的新型框架。GPTree无需特征工程和提示链设计，仅需任务特定提示即可利用树状结构动态划分样本。我们还引入了专家在环反馈机制，通过人工干预优化和重建决策路径，强调人类专业知识与机器智能的协同作用，从而进一步提升模型性能。在初创企业孵化阶段识别"独角兽"企业的任务中，我们的决策树达到了7.8%的准确率，超越了使用少样本学习的gpt-4o模型（3.1%至5.6%）以及最优人类决策者。