Rethinking Knowledge Retrieval for Generation: A Survey on RAG Architectures and Applications
Large Language Models (LLMs) have demonstrated remarkable fluency and versatility across natural language tasks but remain fundamentally limited by their static knowledge and susceptibility to hallucinations, especially in domains requiring up to date or attribute grounded information. Retrieval Augmented Generation (R...
Meghana Sunil, V. Shravya, Shravan Venkatraman et al.
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