SGSH : Stimulate Large Language Models with skeleton heuristics for knowledge base question generation

• Main Authors: GUO, Shasha, LIAO, Lizi, ZHANG, Jing, WANG, Yanling, LI, Cuiping, CHEN, Hong
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2024
• Subjects: Knowledge base question generation; KBQR; Natural language processing; Skeleton Heuristics; Large language models; LLMs; Artificial Intelligence and Robotics; Computer Sciences
• Online Access: https://ink.library.smu.edu.sg/sis_research/9702; https://ink.library.smu.edu.sg/context/sis_research/article/10702/viewcontent/2024.findings_naacl.287.pdf
• Institution: Singapore Management University

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 ad...