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Evaluating the Efficacy of Open-Source LLMs in Enterprise-Specific RAG Systems: A Comparative Study of Performance and Scalability

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arxiv 2406.11424 v1 pith:XELHES35 submitted 2024-06-17 cs.IR cs.CL

Gautam B , Anupam Purwar This is my paper · ORCID
classification cs.IRcs.CL
keywords llmsopen-sourceenterprise-specificperformancedatagenerationintegrationlanguage
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. VLMs-in-the-Wild: Bridging the Gap Between Academic Benchmarks and Enterprise Reality

    cs.CV 2025-09 conditional novelty 6.0 of 10

    VLM-in-the-Wild provides an enterprise-focused benchmark and the BlockWeaver OCR matching algorithm, reporting that a small fine-tuned model can rival a 32B model on some tasks.

  2. Quasiparticle interference in LiFeAs: Signature of inelastic tunneling through spin fluctuations

    cond-mat.supr-con 2025-08 unverdicted novelty 6.0 of 10

    Replica QPI features in LiFeAs are attributed to inelastic tunneling through spin fluctuations at 8 to 10 meV, matching neutron scattering.

  3. AI5GTest: AI-Driven Specification-Aware Automated Testing and Validation of 5G O-RAN Components

    cs.NI 2025-06 conditional novelty 6.0 of 10

    An LLM-based framework that generates expected O-RAN and 3GPP procedural flows from standards and validates captured signaling logs against them, reporting 100% accuracy on 15 testbed instances and under an hour per t...

  4. E-ARMOR: Edge case Assessment and Review of Multilingual Optical Character Recognition

    cs.CL 2025-09 reject novelty 4.0 of 10

    Their custom PaddleOCR-based system achieves the best F1 (0.46), fastest latency (0.17 s/image), and lowest cost ($0.006/1k images) among seven OCR systems on a private 54-language benchmark.

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