Source: ZHUAN ZHI
This article is approximately 1000 words long and is recommended for a 5-minute read.
In this tutorial, we provide a comprehensive review of the existing research on Retrieval-Augmented Large Language Models (RA-LLMs).
As one of the most advanced technologies in the field of artificial intelligence, Retrieval-Augmented Generation (RAG) technology can provide reliable and up-to-date external knowledge, bringing significant convenience to numerous tasks. Especially in the era of AI-generated content (AIGC), RAG’s powerful retrieval capabilities can provide additional knowledge to help existing generative AI produce high-quality outputs. Recently, large language models (LLMs) have demonstrated revolutionary capabilities in language understanding and generation, but they still face inherent limitations, such as hallucinations and outdated internal knowledge. Given RAG’s strong ability to provide the latest and useful auxiliary information, Retrieval-Augmented Large Language Models (RA-LLMs) have emerged, leveraging external authoritative knowledge bases rather than solely relying on the model’s internal knowledge, thus enhancing the generation quality of LLMs.
In this tutorial, we provide a comprehensive review of the existing research on Retrieval-Augmented Large Language Models (RA-LLMs), covering three main technical perspectives: architecture, training strategies, and applications. As foundational knowledge, we briefly introduce the basic principles of LLMs and their recent developments. Next, to demonstrate the practical significance of RAG to LLMs, we categorize mainstream related work by application domain, detailing the challenges faced in each domain and the corresponding capabilities of RA-LLMs. Finally, to provide deeper insights, we discuss the current limitations and several promising directions for future research.
Our review paper: “RAG-Meets-LLMs: Advancing Retrieval-Augmented Large Language Models”
https://advanced-recommender-systems.github.io/RAG-Meets-LLMs/
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