AI RESEARCH
Retrieval-Augmented Generation for Natural Language Processing: A Survey
arXiv CS.CL
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ArXi:2407.13193v4 Announce Type: replace Large language models (LLMs) have achieved strong empirical performance in various fields, benefiting from their huge amount of parameters that knowledge. However, LLMs still suffer from several key issues, such as hallucination problems, knowledge update issues, and lacking domain-specific expertise. The appearance of retrieval-augmented generation (RAG), which leverages an external knowledge base to augment LLMs, mitigates these limitations.