AMATA : Adaptive Multi-Agent Trajectory Alignment for Knowledge-Intensive Question Answering
Taolin Zhang · Dongyang Li · Chen Chen · Qizhou Chen · Jiuheng Wan · Xiaofeng He · Chengyu Wang · Richang Hong
摘要
Despite substantial advances in large language models (LLMs), producing factually consistent responses for knowledge-intensive question answering remains challenging. These difficulties are primarily due to hallucination and the limitations of LLMs in bridging long-tail knowledge gaps. To address...