May 13, 2024, 4:46 a.m. | Yujuan Ding, Wenqi Fan, Liangbo Ning, Shijie Wang, Hengyun Li, Dawei Yin, Tat-Seng Chua, Qing Li

cs.CL updates on arXiv.org arxiv.org

arXiv:2405.06211v1 Announce Type: new
Abstract: As one of the most advanced techniques in AI, Retrieval-Augmented Generation (RAG) techniques can offer reliable and up-to-date external knowledge, providing huge convenience for numerous tasks. Particularly in the era of AI-generated content (AIGC), the powerful capacity of retrieval in RAG in providing additional knowledge enables retrieval-augmented generation to assist existing generative AI in producing high-quality outputs. Recently, large Language Models (LLMs) have demonstrated revolutionary abilities in language understanding and generation, while still facing inherent …

abstract advanced aigc ai-generated content arxiv capacity cs.ai cs.cl cs.ir generated knowledge language language models large language large language models llms rag retrieval retrieval-augmented survey tasks type

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