Haeun Lee  Youngjong Ko

10.5626/JOK.2024.51.11.1020

question answering Open Domain Question Answering generative language model supporting facts generation explainability

"Open domain question answering system requires external knowledge not satisfied by knowledge inherent in the language model to answer a given query. It is a technology that is being studied importantly for solving the hallucination problem that occurs in recent large language models. In this paper, we propose a model that utilizes structural information of Query-attentive Semantic Graph (QSG) to summarize information between distant documents based on a query and utilize it as supporting factors for a multi-document-based question answering system. Query-based supporting factors generated by summarizing can improve answer generation performance and show better explainability than extracted supporting factors."