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In-Context Learning for Knowledge Base Question Answering for Unmanned Systems based on Large Language Models

arXiv 2311.02956 ↗First posted 6 Nov 2023cs.CL cs.AIcited by 0

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Abstract (arXiv)

Knowledge Base Question Answering (KBQA) aims to answer factoid questions based on knowledge bases. However, generating the most appropriate knowledge base query code based on Natural Language Questions (NLQ) poses a significant challenge in KBQA. In this work, we focus on the CCKS2023 Competition of Question Answering with Knowledge Graph Inference for Unmanned Systems. Inspired by the recent success of large language models (LLMs) like ChatGPT and GPT-3 in many QA tasks, we propose a ChatGPT-based Cypher Query Language (CQL) generation framework to generate the most appropriate CQL based on the given NLQ. Our generative framework contains six parts: an auxiliary model predicting the syntax-related information of CQL based on the given NLQ, a proper noun matcher extracting proper nouns from the given NLQ, a demonstration example selector retrieving similar examples of the input sample, a prompt constructor designing the input template of ChatGPT, a ChatGPT-based generation model generating the CQL, and an ensemble model to obtain the final answers from diversified outputs. With our ChatGPT-based CQL generation framework, we achieved the second place in the CCKS 2023 Question Answering with Knowledge Graph Inference for Unmanned Systems competition, achieving an F1-score of 0.92676.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 15 of its 17 references that are in the map. First posted on arXiv 6 Nov 2023.

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References · 15

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  12. 2020Beyond I.I.D.: Three Levels of Generalization for Question Answering on Knowledge Basescited by 171
3 more
  1. 2022Subgraph Retrieval Enhanced Model for Multi-hop Knowledge Base Question Answeringcited by 93
  2. 2023Few-shot In-context Learning for Knowledge Base Question Answeringcited by 74
  3. 2022Knowledge Base Question Answering: A Semantic Parsing Perspectivecited by 22

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