用强化学习训练的LLM，让表格数据预测更准确且可解释。
TabReason: A Reinforcement Learning-Enhanced Reasoning LLM for Explainable Tabular Data Prediction
- 用强化学习优化推理型LLM，引导生成合理预测逻辑。
- 在金融数据集上比现有LLM提升预测准确率，且理由更易懂。
- 适合需要透明决策过程的金融、医疗等高可信场景。
表格数据的预测建模是众多实际应用的基础。尽管梯度提升机和部分近期深度模型在表格数据上表现优异，但通常缺乏可解释性。另一方面，大语言模型（LLMs）展现出生成类人推理与解释的强大能力，但在表格数据预测上仍表现不足。本文提出一种新方法，利用强化学习训练的基于推理的LLM，实现更准确且可解释的表格数据预测。该方法引入定制化奖励函数，引导模型不仅提升预测准确性，还生成人类可理解的预测理由。所提方法在金融基准数据集上进行评估，并与现有主流LLM进行对比。
原文摘要 · Abstract (English)
Predictive modeling on tabular data is the cornerstone of many real-world applications. Although gradient boosting machines and some recent deep models achieve strong performance on tabular data, they often lack interpretability. On the other hand, large language models (LLMs) have demonstrated powerful capabilities to generate human-like reasoning and explanations, but remain under-performed for tabular data prediction. In this paper, we propose a new approach that leverages reasoning-based LLMs, trained using reinforcement learning, to perform more accurate and explainable predictions on tabular data. Our method introduces custom reward functions that guide the model not only toward better prediction accuracy but also toward human-understandable reasons for its predictions. The proposed method is evaluated on financial benchmark datasets and compared against established LLMs.
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