Retrieval-Augmented Generation for Large Language Models: A Survey

Large Language Models (LLMs) showcase impressive capabilities but encounter challenges like hallucination, outdated knowledge, and non-transparent, untraceable reasoning processes. Additionally, this paper introduces up-to-date evaluation framework and benchmark.

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• 1 Retrieval-Augmented Generation (RAG) has emerged as a promising solution by incorporating knowledge from external databases.
• 2 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.
• 3 RAG synergistically merges LLMs' intrinsic knowledge with the vast, dynamic repositories of external databases.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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