When Machine Unlearning Meets Retrieval Augmented Generation Rag Keep Secret Or Forget Knowledge

Retrieval-Augmented Generation (RAG) | LLM Knowledge Base

Retrieval-Augmented Generation (RAG) | LLM Knowledge Base To address these limitations, we propose a lightweight unlearning framework based on retrieval augmented generation (rag) technology. by modifying the external knowledge base of rag, we simulate the effects of forgetting without directly interacting with the unlearned llm. This repository tracks the latest research on machine unlearning in large language models (llms). the goal is to offer a comprehensive list of papers and resources relevant to the topic. as of the last commit, there are 261 papers, 13 surveys and position papers, 1 framework, and 2 blog posts.

(PDF) When Machine Unlearning Meets Retrieval-Augmented Generation (RAG): Keep Secret Or Forget ...

(PDF) When Machine Unlearning Meets Retrieval-Augmented Generation (RAG): Keep Secret Or Forget ... Without this freedom, it is impossible for scientific efforts to be geared toward gaining knowledge and facts. it is therefore extremely worrying that the scientific freedom is coming under increasing pressure in various regions of the world. (read more). These powerful ai systems have demonstrated remarkable abilities in natural language processing, generation, and understanding. however, as llms continue to grow in size and complexity, new challenges have emerged, including the need for more accurate, relevant, and contextual responses. We explore a general purpose fine tuning recipe for retrieval augmented generation (rag) — models which combine pre trained parametric and non parametric memory for language generation. Enter retrieval augmented generation (rag), a paradigm shifting approach that combines the generative power of llms with the factual grounding of information retrieval systems.

Understanding Retrieval-Augmented Generation RAG In Machine Learning

Understanding Retrieval-Augmented Generation RAG In Machine Learning We explore a general purpose fine tuning recipe for retrieval augmented generation (rag) — models which combine pre trained parametric and non parametric memory for language generation. Enter retrieval augmented generation (rag), a paradigm shifting approach that combines the generative power of llms with the factual grounding of information retrieval systems. The fundamental concept behind rag lies in its ability to retrieve relevant information from external databases, documents, or knowledge bases and then use this retrieved information to augment the generation process. Before we get into the nitty gritty, let’s break down what retrieval augmented generation really means. in the simplest terms, rag is a cutting edge approach in deep learning that combines the power of information retrieval with the finesse of language generation. This work proposes a lightweight unlearning framework based on retrieval augmented generation (rag) technology that is particularly effective for closed source llms, where existing unlearning methods often fail. Fig. 2. the overview of rag technology. the left figure gives the workflow of a regular rag framework. the right figure details the workflow of rag based unlearning.

When Machine Unlearning Meets Retrieval-Augmented Generation RAG Keep Secret or Forget Knowledge?