IM-RAG: Multi-Round Retrieval-Augmented Generation Through Learning Inner Monologues

Explore a cutting-edge conference talk on Retrieval Augmented Generation (RAG) presented at SIGIR 2024. Delve into the innovative IM-RAG approach, which introduces multi-round retrieval-augmented generation through learning inner monologues. Discover how authors Diji Yang, Jinmeng Rao, Kezhen Chen, Xiaoyuan Guo, Yawen Zhang, Jie Yang, and Yi Zhang have advanced the field of information retrieval and natural language processing. Gain insights into the potential applications and implications of this novel technique for improving the accuracy and coherence of AI-generated responses. In this 14-minute presentation, learn about the methodology, experimental results, and future directions of IM-RAG, which promises to enhance the capabilities of language models in various domains.

SIGIR 2024 M3.1 [fp] IM-RAG: Multi-Round Retrieval-Augmented Generation Through Learning Inner Mono