In-Context Learning for Knowledge Base Question Answering for Unmanned
  Systems based on Large Language Models
Yunlong Chen Yaming Zhang Jianfei Yu Li Yang Rui Xia
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.