Make a Choice! Knowledge Base Question Answering with In-Context Learning

• URL: http://arxiv.org/abs/2305.13972v1
• Date: Tue, 23 May 2023 11:56:03 GMT
• Title: Make a Choice! Knowledge Base Question Answering with In-Context Learning
• Authors: Chuanyuan Tan, Yuehe Chen, Wenbiao Shao, Wenliang Chen
• Abstract summary: Question answering over knowledge bases (KBQA) aims to answer factoid questions with a given knowledge base (KB) Due to the large scale of KB, annotated data is impossible to cover all fact schemas in KB. We present McL-KBQA, a framework that incorporates the few-shot ability of LLM into the KBQA method via ICL-based multiple choice.
• Score: 1.7827767384590838
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Question answering over knowledge bases (KBQA) aims to answer factoid questions with a given knowledge base (KB). Due to the large scale of KB, annotated data is impossible to cover all fact schemas in KB, which poses a challenge to the generalization ability of methods that require a sufficient amount of annotated data. Recently, LLMs have shown strong few-shot performance in many NLP tasks. We expect LLM can help existing methods improve their generalization ability, especially in low-resource situations. In this paper, we present McL-KBQA, a framework that incorporates the few-shot ability of LLM into the KBQA method via ICL-based multiple choice and then improves the effectiveness of the QA tasks. Experimental results on two KBQA datasets demonstrate the competitive performance of McL-KBQA with strong improvements in generalization. We expect to explore a new way to QA tasks from KBQA in conjunction with LLM, how to generate answers normatively and correctly with strong generalization.

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