TableReasoner：基于大语言模型的表格推理框架进阶
TableReasoner: Advancing Table Reasoning Framework with Large Language Models
AI总结：
针对表格问答中表格规模大、语义不全和实体歧义等挑战，提出基于大语言模型与编程的TableReasoner框架，通过结合结构与语义的schema建模、多步schema链接及迭代思考架构实现高效推理，在SemEval-2025 Task 8两项子任务中均获第一。
摘要：
本文提出了我们为表格问答（TQA）开发的系统。由于真实世界表格数据的特征，如规模庞大、列语义不完整以及实体歧义，TQA任务面临诸多挑战。为解决这些问题，我们提出了一种基于大语言模型（LLM）和编程驱动的表格推理框架，名为TableReasoner。它利用结合结构与语义表示的schema对表格进行建模，从而实现对大型表格的整体理解与高效处理。我们设计了多步schema链接计划，以推导出仅保留与查询相关信息的聚焦表格schema，消除歧义并缓解幻觉现象。该聚焦表格schema为查询优化和编程提供了精确且充分的表格细节。此外，我们将推理工作流整合到迭代思考架构中，实现思考、推理与反思的增量循环。我们的系统在SemEval-2025 Task 8的两个子任务中均获得第一名。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
