TabReason: A Reinforcement Learning-Enhanced Reasoning LLM for Explainable Tabular Data Prediction
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866913918773886976 |
|---|---|
| author | Xu, Tommy Zhang, Zhitian Sun, Xiangyu Zung, Lauren Kelly Hajimirsadeghi, Hossein Mori, Greg |
| author_facet | Xu, Tommy Zhang, Zhitian Sun, Xiangyu Zung, Lauren Kelly Hajimirsadeghi, Hossein Mori, Greg |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_21807 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | TabReason: A Reinforcement Learning-Enhanced Reasoning LLM for Explainable Tabular Data Prediction Xu, Tommy Zhang, Zhitian Sun, Xiangyu Zung, Lauren Kelly Hajimirsadeghi, Hossein Mori, Greg Machine Learning 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. |
| title | TabReason: A Reinforcement Learning-Enhanced Reasoning LLM for Explainable Tabular Data Prediction |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2505.21807 |