用大模型构建可编程的表格推理框架，解决真实表格数据中的歧义与噪声问题。
TableReasoner: Advancing Table Reasoning Framework with Large Language Models
- 基于结构与语义融合的表模式，实现对大规模表格的全局理解。
- 通过多步模式链接提取聚焦查询的表结构，减少幻觉并提升精度。
- 采用迭代思维架构，支持逐步推理与反思，获SemEval-2025双赛道第一。
本文提出一种面向表格问答（TQA）的系统TableReasoner。TQA面临真实表格数据规模大、列语义不完整、实体歧义等挑战。为此，我们设计了一种基于大语言模型（LLM）和编程的表格推理框架，通过融合结构与语义表示的表模式，实现对大型表格的全面理解与高效处理。我们提出多步模式链接方案，生成仅包含查询相关信息的聚焦表模式，消除歧义并缓解幻觉。该聚焦表模式为查询优化与程序生成提供精确且充分的细节。此外，将推理流程整合至迭代思维架构中，支持思考、推理与反思的循环迭代。本系统在SemEval-2025 Task 8的两个子任务中均取得第一名。
原文摘要 · Abstract (English)
The paper presents our system developed for table question answering (TQA). TQA tasks face challenges due to the characteristics of real-world tabular data, such as large size, incomplete column semantics, and entity ambiguity. To address these issues, we propose a large language model (LLM)-powered and programming-based table reasoning framework, named TableReasoner. It models a table using the schema that combines structural and semantic representations, enabling holistic understanding and efficient processing of large tables. We design a multi-step schema linking plan to derive a focused table schema that retains only query-relevant information, eliminating ambiguity and alleviating hallucinations. This focused table schema provides precise and sufficient table details for query refinement and programming. Furthermore, we integrate the reasoning workflow into an iterative thinking architecture, allowing incremental cycles of thinking, reasoning and reflection. Our system achieves first place in both subtasks of SemEval-2025 Task 8.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。