Expansion and Validation of Open-Domain Question and Answering: An Integrated Framework Based on Large Models

Kun Hou, Shiqi Sun, Haiyang Jiang, Haoliang Zhang, Jingyuan Li, Yingying Liu, Haiyang Wang

Abstract: Open-Domain Question Answering (ODQA) has emerged as a critical research area in the field of natural language processing (NLP) in artificial intelligence (AI). Existing approaches primarily follow two paradigms for evidence collection: retrieve-then-read methods often struggle to acquire comprehensive and diverse evidence, and generate-then-read approaches often produce documents that lack contextual accuracy and relevance. We introduce an innovative framework named Expansion Generation and Verification (EGV), derived from the core processes of generating, evaluating, and verifying evidence. EGV encompasses six key stages: generation expansion, expansion evaluation, document re-ranking, re-ranking evaluation, answer generation, and answer verification. This framework effectively integrates the strengths of both retrieval-based and generative evidence collection methodologies. Experimental evaluations on widely-used benchmarks, including NQ, WebQ, and TriviaQA, demonstrate that EGV achieves state-of-the-art performance in both answer accuracy and evidence quality. These results under-score EGV's potential to significantly advance ODQA research and its practical applications.