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RAG

Retrieval-Augmented Generation (RAG) is a task that combines the strengths of both retrieval-based models and generation-based models. In this approach, a retrieval system selects relevant documents or passages from a large corpus, and a generation model, typically a neural language model, uses the retrieved information to generate a response. This method enhances the accuracy and coherence of generated text, especially in tasks requiring detailed knowledge or long context handling.

RAG is particularly useful in open-domain question answering, knowledge-grounded dialogue, and summarization tasks. The retrieval step helps the model to access and incorporate external information, making it less reliant on memorized knowledge and better suited for generating responses based on the latest or domain-specific information.

The performance of RAG systems is usually measured using metrics such as precision, recall, F1 score, BLEU score, and exact match. Some popular datasets for evaluating RAG models include Natural Questions, MS MARCO, TriviaQA, and SQuAD.

Papers

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Showing 851–900 of 2111 papers

TitleStatusHype
Augmenting Large Language Models with Static Code Analysis for Automated Code Quality Improvements—0
Evaluating and Enhancing Large Language Models Performance in Domain-specific Medicine: Osteoarthritis Management with DocOA—0
GE-Chat: A Graph Enhanced RAG Framework for Evidential Response Generation of LLMs—0
GEC-RAG: Improving Generative Error Correction via Retrieval-Augmented Generation for Automatic Speech Recognition Systems—0
GEE-OPs: An Operator Knowledge Base for Geospatial Code Generation on the Google Earth Engine Platform Powered by Large Language Models—0
GEM: Empowering LLM for both Embedding Generation and Language Understanding—0
CALLM: Understanding Cancer Survivors' Emotions and Intervention Opportunities via Mobile Diaries and Context-Aware Language Models—0
GenAI-powered Multi-Agent Paradigm for Smart Urban Mobility: Opportunities and Challenges for Integrating Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) with Intelligent Transportation Systems—0
GenDec: A robust generative Question-decomposition method for Multi-hop reasoning—0
Generating a Low-code Complete Workflow via Task Decomposition and RAG—0
Generating Diverse Q&A Benchmarks for RAG Evaluation with DataMorgana—0
A Pilot Empirical Study on When and How to Use Knowledge Graphs as Retrieval Augmented Generation—0
Generative AI Agent for Next-Generation MIMO Design: Fundamentals, Challenges, and Vision—0
Generative AI in Cybersecurity: A Comprehensive Review of LLM Applications and Vulnerabilities—0
Comparative Analysis of Retrieval Systems in the Real World—0
Generative AI for Low-Carbon Artificial Intelligence of Things with Large Language Models—0
Establishing Performance Baselines in Fine-Tuning, Retrieval-Augmented Generation and Soft-Prompting for Non-Specialist LLM Users—0
Generative AI in the Construction Industry: A State-of-the-art Analysis—0
ESGReveal: An LLM-based approach for extracting structured data from ESG reports—0
Generative Information Retrieval Evaluation—0
Calibrated Decision-Making through LLM-Assisted Retrieval—0
ER-RAG: Enhance RAG with ER-Based Unified Modeling of Heterogeneous Data Sources—0
Generative Sign-description Prompts with Multi-positive Contrastive Learning for Sign Language Recognition—0
GenSco: Can Question Decomposition based Passage Alignment improve Question Answering?—0
GeoCoder: Solving Geometry Problems by Generating Modular Code through Vision-Language Models—0
Comprehensive and Practical Evaluation of Retrieval-Augmented Generation Systems for Medical Question Answering—0
GeoRAG: A Question-Answering Approach from a Geographical Perspective—0
ERATTA: Extreme RAG for Table To Answers with Large Language Models—0
CAISSON: Concept-Augmented Inference Suite of Self-Organizing Neural Networks—0
How Much Can RAG Help the Reasoning of LLM?—0
GLLM: Self-Corrective G-Code Generation using Large Language Models with User Feedback—0
"Glue pizza and eat rocks" -- Exploiting Vulnerabilities in Retrieval-Augmented Generative Models—0
GNN-ACLP: Graph Neural Networks based Analog Circuit Link Prediction—0
CAFE: Retrieval Head-based Coarse-to-Fine Information Seeking to Enhance Multi-Document QA Capability—0
ENWAR: A RAG-empowered Multi-Modal LLM Framework for Wireless Environment Perception—0
GPT-4 as an Agronomist Assistant? Answering Agriculture Exams Using Large Language Models—0
EnronQA: Towards Personalized RAG over Private Documents—0
Cache-Craft: Managing Chunk-Caches for Efficient Retrieval-Augmented Generation—0
Anveshana: A New Benchmark Dataset for Cross-Lingual Information Retrieval On English Queries and Sanskrit Documents—0
Enhancing tutoring systems by leveraging tailored promptings and domain knowledge with Large Language Models—0
Enhancing Tourism Recommender Systems for Sustainable City Trips Using Retrieval-Augmented Generation—0
Enhancing Thyroid Cytology Diagnosis with RAG-Optimized LLMs and Pa-thology Foundation Models—0
C-3PO: Compact Plug-and-Play Proxy Optimization to Achieve Human-like Retrieval-Augmented Generation—0
GraphAide: Advanced Graph-Assisted Query and Reasoning System—0
Answering real-world clinical questions using large language model based systems—0
Graph-based Approaches and Functionalities in Retrieval-Augmented Generation: A Comprehensive Survey—0
Advancing Conversational Psychotherapy: Integrating Privacy, Dual-Memory, and Domain Expertise with Large Language Models—0
Graph-Based Retriever Captures the Long Tail of Biomedical Knowledge—0
How to Build an AI Tutor That Can Adapt to Any Course Using Knowledge Graph-Enhanced Retrieval-Augmented Generation (KG-RAG)—0
Building Trustworthy AI: Transparent AI Systems via Large Language Models, Ontologies, and Logical Reasoning (TranspNet)—0
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