StructGPT: A General Framework for Large Language Model to Reason over Structured Data

In this paper, we aim to improve the reasoning ability of large language models (LLMs) over structured data in a unified way. Inspired by the studies on tool augmentation for LLMs, we develop an Iterative Reading-then-Reasoning (IRR) framework to solve question answering tasks based on structured data, called StructGPT. In this framework, we construct the specialized interfaces to collect relevant evidence from structured data (i.e., reading), and let LLMs concentrate on the reasoning task based on the collected information (i.e., reasoning). Specially, we propose an invokinglinearization-generation procedure to support LLMs in reasoning on the structured data with the help of the interfaces. By iterating this procedure with provided interfaces, our approach can gradually approach the target answers to a given query. Experiments conducted on three types of structured data show that StructGPT greatly improves the performance of LLMs, under the few-shot and zero-shot settings. Our codes and data are publicly available at https://github.com/RUCAIBox/StructGPT . Recently, large language models (LLMs) (Brown et al., 2020; Zhao et al., 2023) have made remarkable advancements in the NLP field. Existing work (Ouyang et al., 2022a; Zhang et al., 2022) has demonstrated that LLMs (e.g., ChatGPT or GPT-4 (OpenAI, 2023)) have strong zero-shot capability to solve a broad range of tasks using specially designed prompts, without task-specific fine-tuning. Despite the successes, recent work has also revealed that LLMs may generate unfaithful information in conflict with the factual knowledge (Li et al., 2023) , and also fall short of mastering domainspecific or real-time knowledge (Schick et al., 2023; Peng et al., 2023) . A direct solution to the above * Equal contributions.