Enhancing Language Models with Retrieval-Augmented Generation for Accurate and Contextual Responses
Prof. Mauro Mazzei
CNR IAS, Italy
Abstract: The advent of Large Language Models (LLMs) has revolutionized natural language processing, enabling the generation of coherent and contextually relevant text. However, these models often suffer from inaccuracies and hallucinations, particularly when dealing with domains outside their training data. Retrieval-Augmented Generation (RAG) addresses these limitations by integrating the generative capabilities of LLMs with a retrieval mechanism that sources relevant information from reliable and up-to-date knowledge bases. The integration of RAG with LLMs represents a significant advancement in artificial intelligence, offering enhanced accuracy and contextual relevance in generated responses. This paper explores the architecture and implementation of RAG systems, highlighting their ability to enhance the accuracy and relevance of generated responses. Key components of RAG include the use of vector databases for efficient information retrieval and the application of semantic search techniques to ensure high precision in context retrieval. The paper also examines the practical advantages of RAG, such as reduced computational costs and the ability to provide current information without the need for frequent model retraining. Through a detailed case study of an open-source framework leveraging RAG technology, we discuss the setup and configuration on cloud infrastructure, the impact of parameter adjustments on response quality, and the practical applications of RAG in various domains.
Brief Biography of the Speaker: To be anancioud soon