SGSH: Stimulate Large Language Models with Skeleton Heuristics for Knowledge Base Question Generation

arXiv:2404.01923v1 Announce Type: new
Abstract: Knowledge base question generation (KBQG) aims to generate natural language questions from a set of triplet facts extracted from KB. Existing methods have significantly boosted the performance of KBQG via pre-trained language models (PLMs) thanks to the richly endowed semantic knowledge. With the advance of pre-training techniques, large language models (LLMs) (e.g., GPT-3.5) undoubtedly possess much more semantic knowledge. Therefore, how to effectively organize and exploit the abundant knowledge for KBQG becomes the focus of …

abstract arxiv cs.ai cs.cl facts generate heuristics knowledge knowledge base language language models large language large language models natural natural language performance question questions semantic set type via