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
- "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) - Machine Against the RAG: Jamming Retrieval-Augmented Generation with Blocker Documents [17.95339197094059]
Retrieval-augmented generation (RAG) systems respond to queries by retrieving relevant documents from a knowledge database, then generating an answer by applying an LLM to the retrieved documents.
We demonstrate that RAG systems that operate on databases with potentially untrusted content are vulnerable to a new class of denial-of-service attacks we call jamming.
arXiv Detail & Related papers (2024-06-09T17:55:55Z) - Phantom: General Trigger Attacks on Retrieval Augmented Language Generation [30.63258739968483]
We propose new attack surfaces for an adversary to compromise a victim's RAG system.
The first step involves crafting a poisoned document designed to be retrieved by the RAG system.
In the second step, a specially crafted adversarial string within the poisoned document triggers various adversarial attacks.
arXiv Detail & Related papers (2024-05-30T21:19:24Z) - 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 [122.62012375722124]
In existing methods, large language models (LLMs) cannot precisely assess the relevance of retrieved documents.
We propose REAR, a RElevance-Aware Retrieval-augmented approach for open-domain question answering (QA)
arXiv Detail & Related papers (2024-02-27T13:22:51Z) - Self-Retrieval: Building an Information Retrieval System with One Large
Language Model [102.78988790457004]
Self-Retrieval is an end-to-end, LLM-driven information retrieval architecture.
We show that Self-Retrieval significantly outperforms previous retrieval approaches by a large margin.
arXiv Detail & Related papers (2024-02-23T18:45:35Z) - 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.