GEM-RAG: Graphical Eigen Memories For Retrieval Augmented Generation
- URL: http://arxiv.org/abs/2409.15566v1
- Date: Mon, 23 Sep 2024 21:42:47 GMT
- Title: GEM-RAG: Graphical Eigen Memories For Retrieval Augmented Generation
- Authors: Brendan Hogan Rappazzo, Yingheng Wang, Aaron Ferber, Carla Gomes,
- Abstract summary: We introduce Graphical Eigen Memories For Retrieval Augmented Generation (GEM-RAG)
GEM-RAG works by tagging each chunk of text in a given text corpus with LLM generated utility'' questions.
We evaluate GEM-RAG, using both UnifiedQA and GPT-3.5 Turbo as the LLMs, with SBERT, and OpenAI's text encoders on two standard QA tasks.
- Score: 3.2027710059627545
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The ability to form, retrieve, and reason about memories in response to stimuli serves as the cornerstone for general intelligence - shaping entities capable of learning, adaptation, and intuitive insight. Large Language Models (LLMs) have proven their ability, given the proper memories or context, to reason and respond meaningfully to stimuli. However, they are still unable to optimally encode, store, and retrieve memories - the ability to do this would unlock their full ability to operate as AI agents, and to specialize to niche domains. To remedy this, one promising area of research is Retrieval Augmented Generation (RAG), which aims to augment LLMs by providing them with rich in-context examples and information. In question-answering (QA) applications, RAG methods embed the text of interest in chunks, and retrieve the most relevant chunks for a prompt using text embeddings. Motivated by human memory encoding and retrieval, we aim to improve over standard RAG methods by generating and encoding higher-level information and tagging the chunks by their utility to answer questions. We introduce Graphical Eigen Memories For Retrieval Augmented Generation (GEM-RAG). GEM-RAG works by tagging each chunk of text in a given text corpus with LLM generated ``utility'' questions, connecting chunks in a graph based on the similarity of both their text and utility questions, and then using the eigendecomposition of the memory graph to build higher level summary nodes that capture the main themes of the text. We evaluate GEM-RAG, using both UnifiedQA and GPT-3.5 Turbo as the LLMs, with SBERT, and OpenAI's text encoders on two standard QA tasks, showing that GEM-RAG outperforms other state-of-the-art RAG methods on these tasks. We also discuss the implications of having a robust RAG system and future directions.
Related papers
- TrustRAG: An Information Assistant with Retrieval Augmented Generation [73.84864898280719]
TrustRAG is a novel framework that enhances acRAG from three perspectives: indexing, retrieval, and generation.
We open-source the TrustRAG framework and provide a demonstration studio designed for excerpt-based question answering tasks.
arXiv Detail & Related papers (2025-02-19T13:45:27Z) - Knowledge Graph-Guided Retrieval Augmented Generation [34.83235788116369]
We propose a Knowledge Graph-Guided Retrieval Augmented Generation framework.
KG$2$RAG provides fact-level relationships between chunks, improving the diversity and coherence of the retrieved results.
arXiv Detail & Related papers (2025-02-08T02:14:31Z) - QuIM-RAG: Advancing Retrieval-Augmented Generation with Inverted Question Matching for Enhanced QA Performance [1.433758865948252]
This work presents a novel architecture for building Retrieval-Augmented Generation (RAG) systems.
RAG architecture is constructed to generate responses from the target document.
We introduce QuIM-RAG, a novel approach for the retrieval mechanism in our system.
arXiv Detail & Related papers (2025-01-06T01:07:59Z) - G-RAG: Knowledge Expansion in Material Science [0.0]
Graph RAG integrates graph databases to enhance the retrieval process.
We implement an agent-based parsing technique to achieve a more detailed representation of the documents.
arXiv Detail & Related papers (2024-11-21T21:22:58Z) - LLM-Ref: Enhancing Reference Handling in Technical Writing with Large Language Models [4.1180254968265055]
We present LLM-Ref, a writing assistant tool that aids researchers in writing articles from multiple source documents.
Unlike traditional RAG systems that use chunking and indexing, our tool retrieves and generates content directly from text paragraphs.
Our approach achieves a $3.25times$ to $6.26times$ increase in Ragas score, a comprehensive metric that provides a holistic view of a RAG system's ability to produce accurate, relevant, and contextually appropriate responses.
arXiv Detail & Related papers (2024-11-01T01:11:58Z) - VisRAG: Vision-based Retrieval-augmented Generation on Multi-modality Documents [66.42579289213941]
Retrieval-augmented generation (RAG) is an effective technique that enables large language models to utilize external knowledge sources for generation.
In this paper, we introduce VisRAG, which tackles this issue by establishing a vision-language model (VLM)-based RAG pipeline.
In this pipeline, instead of first parsing the document to obtain text, the document is directly embedded using a VLM as an image and then retrieved to enhance the generation of a VLM.
arXiv Detail & Related papers (2024-10-14T15:04:18Z) - Retriever-and-Memory: Towards Adaptive Note-Enhanced Retrieval-Augmented Generation [72.70046559930555]
We propose a generic RAG approach called Adaptive Note-Enhanced RAG (Adaptive-Note) for complex QA tasks.
Specifically, Adaptive-Note introduces an overarching view of knowledge growth, iteratively gathering new information in the form of notes.
In addition, we employ an adaptive, note-based stop-exploration strategy to decide "what to retrieve and when to stop" to encourage sufficient knowledge exploration.
arXiv Detail & Related papers (2024-10-11T14:03:29Z) - Embodied-RAG: General Non-parametric Embodied Memory for Retrieval and Generation [69.01029651113386]
Embodied-RAG is a framework that enhances the model of an embodied agent with a non-parametric memory system.
At its core, Embodied-RAG's memory is structured as a semantic forest, storing language descriptions at varying levels of detail.
We demonstrate that Embodied-RAG effectively bridges RAG to the robotics domain, successfully handling over 250 explanation and navigation queries.
arXiv Detail & Related papers (2024-09-26T21:44:11Z) - MemoRAG: Moving towards Next-Gen RAG Via Memory-Inspired Knowledge Discovery [24.38640001674072]
Retrieval-Augmented Generation (RAG) leverages retrieval tools to access external databases.
Existing RAG systems are primarily effective for straightforward question-answering tasks.
We propose MemoRAG, a novel retrieval-augmented generation paradigm empowered by long-term memory.
arXiv Detail & Related papers (2024-09-09T13:20:31Z) - Self-RAG: Learning to Retrieve, Generate, and Critique through
Self-Reflection [74.51523859064802]
We introduce a new framework called Self-Reflective Retrieval-Augmented Generation (Self-RAG)
Self-RAG enhances an LM's quality and factuality through retrieval and self-reflection.
It significantly outperforms state-of-the-art LLMs and retrieval-augmented models on a diverse set of tasks.
arXiv Detail & Related papers (2023-10-17T18:18:32Z) - Harnessing Explanations: LLM-to-LM Interpreter for Enhanced
Text-Attributed Graph Representation Learning [51.90524745663737]
A key innovation is our use of explanations as features, which can be used to boost GNN performance on downstream tasks.
Our method achieves state-of-the-art results on well-established TAG datasets.
Our method significantly speeds up training, achieving a 2.88 times improvement over the closest baseline on ogbn-arxiv.
arXiv Detail & Related papers (2023-05-31T03:18:03Z)
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.