An In-Context Schema Understanding Method for Knowledge Base Question Answering

作     者：Liu, Yantao Li, Zixuan Jin, Xiaolong Guo, Yucao Bai, Long Guan, Saiping Guo, Jiafeng Cheng, Xueqi 

作者机构：School of Computer Science and Technology University of Chinese Academy of Sciences CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2023年

核心收录：

主　　题：Knowledge based systems 

摘      要：The Knowledge Base Question Answering (KBQA) task aims to answer natural language questions based on a given knowledge base. Recently, Large Language Models (LLMs) have shown strong capabilities in language understanding and can be used to solve this task. In doing so, a major challenge for LLMs is to overcome the immensity and heterogeneity of knowledge base schemas. Existing methods bypass this challenge by initially employing LLMs to generate drafts of logic forms without schema-specific details. Then, an extra module is used to inject schema information to these drafts. In contrast, in this paper, we propose a simple In-Context Schema Understanding (ICSU) method that enables LLMs to directly understand schemas by leveraging in-context learning. Specifically, ICSU provides schema information to LLMs using schema-related annotated examples. We investigate three example retrieval strategies based on raw questions, anonymized questions, and generated SPARQL queries. Experimental results show that ICSU demonstrates competitive performance compared to baseline methods on both the KQA Pro and WebQSP datasets. © 2023, CC BY.