Evaluating the Efficacy of Open-Source LLMs in Enterprise-Specific RAG Systems: A Comparative Study of Performance and Scalability
This addresses the need for cost-effective and accessible RAG systems for enterprises using their own data, but it is incremental as it focuses on comparing existing open-source models rather than introducing new methods.
This paper tackled the problem of evaluating open-source large language models (LLMs) in enterprise-specific Retrieval-Augmented Generation (RAG) systems, finding that they can significantly improve accuracy and efficiency, offering a viable alternative to proprietary solutions.
This paper presents an analysis of open-source large language models (LLMs) and their application in Retrieval-Augmented Generation (RAG) tasks, specific for enterprise-specific data sets scraped from their websites. With the increasing reliance on LLMs in natural language processing, it is crucial to evaluate their performance, accessibility, and integration within specific organizational contexts. This study examines various open-source LLMs, explores their integration into RAG frameworks using enterprise-specific data, and assesses the performance of different open-source embeddings in enhancing the retrieval and generation process. Our findings indicate that open-source LLMs, combined with effective embedding techniques, can significantly improve the accuracy and efficiency of RAG systems, offering a viable alternative to proprietary solutions for enterprises.