A Simple Architecture for Enterprise Large Language Model Applications based on Role based security and Clearance Levels using Retrieval-Augmented Generation or Mixture of Experts

• URL: http://arxiv.org/abs/2407.06718v1
• Date: Tue, 9 Jul 2024 09:46:23 GMT
• Title: A Simple Architecture for Enterprise Large Language Model Applications based on Role based security and Clearance Levels using Retrieval-Augmented Generation or Mixture of Experts
• Authors: Atilla Özgür, Yılmaz Uygun,
• Abstract summary: The proposed architecture could be used while utilizing Retrieval-Augmented Generation (RAG) and fine tuning of Mixture of experts models (MoE) Using roles and security clearance level of the user, documents in RAG and experts in MoE are filtered.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This study proposes a simple architecture for Enterprise application for Large Language Models (LLMs) for role based security and NATO clearance levels. Our proposal aims to address the limitations of current LLMs in handling security and information access. The proposed architecture could be used while utilizing Retrieval-Augmented Generation (RAG) and fine tuning of Mixture of experts models (MoE). It could be used only with RAG, or only with MoE or with both of them. Using roles and security clearance level of the user, documents in RAG and experts in MoE are filtered. This way information leakage is prevented.

Related papers

• mR$^2$AG: Multimodal Retrieval-Reflection-Augmented Generation for Knowledge-Based VQA [78.45521005703958]
  multimodal Retrieval-Augmented Generation (mRAG) is naturally introduced to provide MLLMs with comprehensive and up-to-date knowledge. We propose a novel framework called textbfRetrieval-textbfReftextbfAugmented textbfGeneration (mR$2$AG) which achieves adaptive retrieval and useful information localization. mR$2$AG significantly outperforms state-of-the-art MLLMs on INFOSEEK and Encyclopedic-VQA
  arXiv  Detail & Related papers  (2024-11-22T16:15:50Z)
• "Glue pizza and eat rocks" -- Exploiting Vulnerabilities in Retrieval-Augmented Generative Models [74.05368440735468]
  Retrieval-Augmented Generative (RAG) models enhance Large Language Models (LLMs) In this paper, we demonstrate a security threat where adversaries can exploit the openness of these knowledge bases.
  arXiv  Detail & Related papers  (2024-06-26T05:36:23Z)
• A + B: A General Generator-Reader Framework for Optimizing LLMs to Unleash Synergy Potential [20.1396255995056]
  Retrieval-Augmented Generation (RAG) is an effective solution to supplement necessary knowledge to large language models (LLMs) "generate-then-read" pipeline is proposed to replace the retrieval stage with generation from the LLM itself. This paper formalizes a general "A + B" framework with varying combinations of foundation models and types for systematic investigation.
  arXiv  Detail & Related papers  (2024-06-06T11:14:27Z)
• ERATTA: Extreme RAG for Table To Answers with Large Language Models [1.3318204310917532]
  Large language models (LLMs) with retrieval augmented-generation (RAG) have been the optimal choice for scalable generative AI solutions. We propose a unique LLM-based system where multiple LLMs can be invoked to enable data authentication, user-query routing, data-retrieval and custom prompting for question-answering capabilities from Enterprise-data tables. Our proposed system and scoring metrics achieve >90% confidence scores across hundreds of user queries in the sustainability, financial health and social media domains.
  arXiv  Detail & Related papers  (2024-05-07T02:49:59Z)
• Detectors for Safe and Reliable LLMs: Implementations, Uses, and Limitations [76.19419888353586]
  Large language models (LLMs) are susceptible to a variety of risks, from non-faithful output to biased and toxic generations. We present our efforts to create and deploy a library of detectors: compact and easy-to-build classification models that provide labels for various harms.
  arXiv  Detail & Related papers  (2024-03-09T21:07:16Z)
• REAR: A Relevance-Aware Retrieval-Augmented Framework for Open-Domain Question Answering [115.72130322143275]
  REAR is a RElevance-Aware Retrieval-augmented approach for open-domain question answering (QA) We develop a novel architecture for LLM-based RAG systems, by incorporating a specially designed assessment module. Experiments on four open-domain QA tasks show that REAR significantly outperforms previous a number of competitive RAG approaches.
  arXiv  Detail & Related papers  (2024-02-27T13:22:51Z)
• T-RAG: Lessons from the LLM Trenches [7.545277950323593]
  Application area is question answering over private enterprise documents. Retrieval-Augmented Generation is most prominent framework for building LLM-based applications. System, which we call Tree-RAG (T-RAG), uses a tree structure to represent entity hierarchies.
  arXiv  Detail & Related papers  (2024-02-12T08:45:08Z)
• RAGAS: Automated Evaluation of Retrieval Augmented Generation [25.402461447140823]
  RAGAs is a framework for evaluation of Retrieval Augmented Generation pipelines. RAG systems are composed of a retrieval and an LLM based generation module.
  arXiv  Detail & Related papers  (2023-09-26T19:23:54Z)
• Recommender AI Agent: Integrating Large Language Models for Interactive Recommendations [53.76682562935373]
  We introduce an efficient framework called textbfInteRecAgent, which employs LLMs as the brain and recommender models as tools. InteRecAgent achieves satisfying performance as a conversational recommender system, outperforming general-purpose LLMs.
  arXiv  Detail & Related papers  (2023-08-31T07:36:44Z)
• Do-Not-Answer: A Dataset for Evaluating Safeguards in LLMs [59.596335292426105]
  This paper collects the first open-source dataset to evaluate safeguards in large language models. We train several BERT-like classifiers to achieve results comparable with GPT-4 on automatic safety evaluation.
  arXiv  Detail & Related papers  (2023-08-25T14:02:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.