TabReason: A Reinforcement Learning-Enhanced Reasoning LLM for Explainable Tabular Data Prediction

2025-05-27

AI Summary

Table prediction faces a fundamental trade-off between accuracy and interpretability: while gradient-boosting models (e.g., XGBoost) achieve high predictive performance, they lack intrinsic interpretability; conversely, large language models (LLMs) exhibit strong reasoning capabilities but underperform on structured tabular tasks. To address this, we propose an interpretability-aware reinforcement learning framework that jointly optimizes prediction accuracy and natural language explanation quality via Proximal Policy Optimization (PPO). Our approach introduces a novel multi-objective reward mechanism and integrates table-aware semantic encoding, instruction alignment, and Chain-of-Thought fine-tuning. This framework overcomes LLMs’ limitations in modeling structured data: on financial benchmark datasets, it matches the predictive accuracy of state-of-the-art gradient-boosting models while generating faithful, coherent, and human-understandable textual explanations—achieving, for the first time, a principled unification of high accuracy and strong interpretability.

Technology Category

• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models
• Machine Learning: Transparent, Interpretable, Explainable ML
• Humans and AI: Explainable AI (XAI) for Human Understanding

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

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 high prediction accuracy but also toward human-understandable reasons for its predictions. Experimental results show that our model achieves promising performance on financial benchmark datasets, outperforming most existing LLMs.

Problem

• Improving accuracy and interpretability of tabular data predictions
• Combining reasoning-based LLMs with reinforcement learning
• Enhancing explainability in financial benchmark datasets

Innovation

• Reinforcement learning-trained reasoning LLM
• Custom reward functions for accuracy and explainability
• Improved performance on financial tabular data