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Retrieval-Augmented Generation for Large Language Models: A Survey
March 28, 2024, 4:48 a.m. | Yunfan Gao, Yun Xiong, Xinyu Gao, Kangxiang Jia, Jinliu Pan, Yuxi Bi, Yi Dai, Jiawei Sun, Meng Wang, Haofen Wang
cs.CL updates on arXiv.org arxiv.org
Abstract: Large Language Models (LLMs) showcase impressive capabilities but encounter challenges like hallucination, outdated knowledge, and non-transparent, untraceable reasoning processes. Retrieval-Augmented Generation (RAG) has emerged as a promising solution by incorporating knowledge from external databases. This enhances the accuracy and credibility of the generation, particularly for knowledge-intensive tasks, and allows for continuous knowledge updates and integration of domain-specific information. RAG synergistically merges LLMs' intrinsic knowledge with the vast, dynamic repositories of external databases. This comprehensive review …
abstract accuracy arxiv capabilities challenges cs.ai cs.cl databases hallucination knowledge language language models large language large language models llms processes rag reasoning retrieval retrieval-augmented solution survey tasks transparent type
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