用大模型长程规划提升表格理解能力，解决多步推理中的遗漏约束问题。
Planning for Success: Exploring LLM Long-term Planning Capabilities in Table Understanding
- 利用大模型的长程规划能力，使推理步骤紧密衔接、目标明确。
- 在WikiTableQuestions和TabFact上达到当前最优性能，显著优于基线方法。
- 适合需要复杂多步推理的表格问答与事实验证任务研究者参考。
表格理解是解决表格问答和事实验证等下游任务的关键。现有方法多依赖思维链（Chain-of-Thought）和问题分解来处理复杂问题，但常缺乏显式长程规划，且步骤间连接薄弱，易遗漏问题约束。本文提出利用大语言模型（LLMs）的长程规划能力增强表格理解，使推理步骤紧密关联并服务于最终目标，克服了传统方法的不足。此外，该方法有效减少了为达成短期目标而引入的冗余信息，提升了推理效率。大量实验表明，本方法在WikiTableQuestions和TabFact数据集上均超越强基线，达到当前最优水平。
原文摘要 · Abstract (English)
Table understanding is key to addressing challenging downstream tasks such as table-based question answering and fact verification. Recent works have focused on leveraging Chain-of-Thought and question decomposition to solve complex questions requiring multiple operations on tables. However, these methods often suffer from a lack of explicit long-term planning and weak inter-step connections, leading to miss constraints within questions. In this paper, we propose leveraging the long-term planning capabilities of large language models (LLMs) to enhance table understanding. Our approach enables the execution of a long-term plan, where the steps are tightly interconnected and serve the ultimate goal, an aspect that methods based on Chain-of-Thought and question decomposition lack. In addition, our method effectively minimizes the inclusion of unnecessary details in the process of solving the next short-term goals, a limitation of methods based on Chain-of-Thought. Extensive experiments demonstrate that our method outperforms strong baselines and achieves state-of-the-art performance on WikiTableQuestions and TabFact datasets.
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