An In-Context Schema Understanding Method for Knowledge Base Question Answering
CoRR(2023)
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.
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关键词
knowledge base question
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