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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
A Survey of Context Engineering for Large Language Models—0
ConfusedPilot: Confused Deputy Risks in RAG-based LLMs—0
Astute RAG: Overcoming Imperfect Retrieval Augmentation and Knowledge Conflicts for Large Language Models—0
Aggregated Knowledge Model: Enhancing Domain-Specific QA with Fine-Tuned and Retrieval-Augmented Generation Models—0
Good/Evil Reputation Judgment of Celebrities by LLMs via Retrieval Augmented Generation—0
GNN-ACLP: Graph Neural Networks based Analog Circuit Link Prediction—0
A Study on the Implementation of Generative AI Services Using an Enterprise Data-Based LLM Application Architecture—0
ConceptFormer: Towards Efficient Use of Knowledge-Graph Embeddings in Large Language Models—0
Conan-embedding: General Text Embedding with More and Better Negative Samples—0
A Study on the Implementation Method of an Agent-Based Advanced RAG System Using Graph—0
Agent-UniRAG: A Trainable Open-Source LLM Agent Framework for Unified Retrieval-Augmented Generation Systems—0
A Collaborative Multi-Agent Approach to Retrieval-Augmented Generation Across Diverse Data—0
Compressing Long Context for Enhancing RAG with AMR-based Concept Distillation—0
Comprehensive and Practical Evaluation of Retrieval-Augmented Generation Systems for Medical Question Answering—0
ASTRID -- An Automated and Scalable TRIaD for the Evaluation of RAG-based Clinical Question Answering Systems—0
Generative Sign-description Prompts with Multi-positive Contrastive Learning for Sign Language Recognition—0
Composing Open-domain Vision with RAG for Ocean Monitoring and Conservation—0
Natural Language Programming in Medicine: Administering Evidence Based Clinical Workflows with Autonomous Agents Powered by Generative Large Language Models—0
Complex System Diagnostics Using a Knowledge Graph-Informed and Large Language Model-Enhanced Framework—0
ASTRAL: Automated Safety Testing of Large Language Models—0
Generative Information Retrieval Evaluation—0
Generative AI Is Not Ready for Clinical Use in Patient Education for Lower Back Pain Patients, Even With Retrieval-Augmented Generation—0
Generative AI in the Construction Industry: A State-of-the-art Analysis—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
Generative AI for Software Architecture. Applications, Trends, Challenges, and Future Directions—0
GeoRAG: A Question-Answering Approach from a Geographical Perspective—0
Comparing the Utility, Preference, and Performance of Course Material Search Functionality and Retrieval-Augmented Generation Large Language Model (RAG-LLM) AI Chatbots in Information-Seeking Tasks—0
Generative AI for Low-Carbon Artificial Intelligence of Things with Large Language Models—0
Generative AI in Cybersecurity: A Comprehensive Review of LLM Applications and Vulnerabilities—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
Generative AI Agent for Next-Generation MIMO Design: Fundamentals, Challenges, and Vision—0
Comparative Analysis of Retrieval Systems in the Real World—0
Assessing the Robustness of Retrieval-Augmented Generation Systems in K-12 Educational Question Answering with Knowledge Discrepancies—0
GPT-4 as an Agronomist Assistant? Answering Agriculture Exams Using Large Language Models—0
GPT vs RETRO: Exploring the Intersection of Retrieval and Parameter-Efficient Fine-Tuning—0
AgentNet: Decentralized Evolutionary Coordination for LLM-based Multi-Agent Systems—0
BadJudge: Backdoor Vulnerabilities of LLM-as-a-Judge—0
Generating Diverse Q&A Benchmarks for RAG Evaluation with DataMorgana—0
Generating a Low-code Complete Workflow via Task Decomposition and RAG—0
GenDec: A robust generative Question-decomposition method for Multi-hop reasoning—0
Command A: An Enterprise-Ready Large Language Model—0
Assessing the Performance of Human-Capable LLMs -- Are LLMs Coming for Your Job?—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
Graph-based Approaches and Functionalities in Retrieval-Augmented Generation: A Comprehensive Survey—0
GEM-RAG: Graphical Eigen Memories For Retrieval Augmented Generation—0
Graph-Based Retriever Captures the Long Tail of Biomedical Knowledge—0
GEM: Empowering LLM for both Embedding Generation and Language Understanding—0
GEE-OPs: An Operator Knowledge Base for Geospatial Code Generation on the Google Earth Engine Platform Powered by Large Language Models—0
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