In this paper, we study how to improve the zero-shot reasoning ability of large language models~(LLMs) over structured data in a unified way. Inspired by the study on tool augmentation for LLMs, we develop an \emph{Iterative Reading-then-Reasoning~(IRR)} approach for solving question answering tasks based on structured data, called \textbf{StructGPT}. In our approach, we construct the specialized function to collect relevant evidence from structured data (\ie \emph{reading}), and let LLMs concentrate the reasoning task based on the collected information (\ie \emph{reasoning}). Specially, we propose an \emph{invoking-linearization-generation} procedure to support LLMs in reasoning on the structured data with the help of the external interfaces. By iterating this procedures with provided interfaces, our approach can gradually approach the target answer to a given query. Extensive experiments conducted on three types of structured data demonstrate the effectiveness of our approach, which can significantly boost the performance of ChatGPT and achieve comparable performance against the full-data supervised-tuning baselines. Our codes and data are publicly available at~\url{https://github.com/RUCAIBox/StructGPT}.

本文研究如何以统一的方式提高大型语言模型在结构化数据上的零-shot推理能力。作者基于工具增强的研究开发了一种名为StructGPT的迭代阅读-推理方法，通过构建收集相关证据的专门函数以及使用外部接口效仿并线性化生成推理，逐步靠近所给定查询的目标答案。对三种类型的结构化数据进行的大量实验表明，该方法能显著提高ChatGPT的表现，并达到与完整数据监督调整基线相当的表现水平。

StructGPT: 大型语言模型推理结构化数据的通用框架