评估开源 LLM 在企业特定 RAG 系统中的功效：性能与可扩展性的比较研究
Evaluating the Efficacy of Open-Source LLMs in Enterprise-Specific RAG Systems: A Comparative Study of Performance and Scalability
AI总结：
本文比较多种开源 LLM 与开源嵌入在基于企业网站数据的 RAG 框架中的集成表现，证明其可显著提升准确性与效率，是专有方案的可行替代。
摘要：
本文分析了开源大型语言模型（LLM）及其在检索增强生成（RAG）任务中的应用，这些任务专门针对从企业网站抓取的企业特定数据集。随着自然语言处理中对 LLM 的依赖日益增加，评估其在特定组织环境中的性能、可访问性和集成至关重要。本研究考察了多种开源 LLM，探讨了如何利用企业特定数据将其集成到 RAG 框架中，并评估了不同开源嵌入在增强检索与生成过程方面的性能。研究结果表明，开源 LLM 结合有效的嵌入技术，能够显著提升 RAG 系统的准确性和效率，为企业提供一种可替代专有解决方案的可行选择。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
