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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 751–800 of 2111 papers

TitleStatusHype
Evaluating Self-Generated Documents for Enhancing Retrieval-Augmented Generation with Large Language Models—0
Generative AI Agent for Next-Generation MIMO Design: Fundamentals, Challenges, and Vision—0
Evaluating Retrieval Augmented Generative Models for Document Queries in Transportation Safety—0
Can Language Models Enable In-Context Database?—0
FABULA: Intelligence Report Generation Using Retrieval-Augmented Narrative Construction—0
Face4RAG: Factual Consistency Evaluation for Retrieval Augmented Generation in Chinese—0
Chain-of-Rank: Enhancing Large Language Models for Domain-Specific RAG in Edge Device—0
Facilitating Video Story Interaction with Multi-Agent Collaborative System—0
Fact-Aware Multimodal Retrieval Augmentation for Accurate Medical Radiology Report Generation—0
Fact-checking AI-generated news reports: Can LLMs catch their own lies?—0
Evaluating Quality of Answers for Retrieval-Augmented Generation: A Strong LLM Is All You Need—0
FACTOID: FACtual enTailment fOr hallucInation Detection—0
Can GPT Redefine Medical Understanding? Evaluating GPT on Biomedical Machine Reading Comprehension—0
Application of NotebookLM, a Large Language Model with Retrieval-Augmented Generation, for Lung Cancer Staging—0
Evaluating Memory in LLM Agents via Incremental Multi-Turn Interactions—0
Evaluating Knowledge Graph Based Retrieval Augmented Generation Methods under Knowledge Incompleteness—0
CancerKG.ORG A Web-scale, Interactive, Verifiable Knowledge Graph-LLM Hybrid for Assisting with Optimal Cancer Treatment and Care—0
Evaluating Consistencies in LLM responses through a Semantic Clustering of Question Answering—0
Bias Evaluation and Mitigation in Retrieval-Augmented Medical Question-Answering Systems—0
AppAgent v2: Advanced Agent for Flexible Mobile Interactions—0
FastRAG: Retrieval Augmented Generation for Semi-structured Data—0
Advancing Retrieval-Augmented Generation for Persian: Development of Language Models, Comprehensive Benchmarks, and Best Practices for Optimization—0
Faux Polyglot: A Study on Information Disparity in Multilingual Large Language Models—0
FeB4RAG: Evaluating Federated Search in the Context of Retrieval Augmented Generation—0
Federated In-Context LLM Agent Learning—0
Federated Learning and RAG Integration: A Scalable Approach for Medical Large Language Models—0
Generative AI in Cybersecurity: A Comprehensive Review of LLM Applications and Vulnerabilities—0
Federated Retrieval-Augmented Generation: A Systematic Mapping Study—0
Federated Retrieval Augmented Generation for Multi-Product Question Answering—0
Generative AI Is Not Ready for Clinical Use in Patient Education for Lower Back Pain Patients, Even With Retrieval-Augmented Generation—0
Few-Shot Fairness: Unveiling LLM's Potential for Fairness-Aware Classification—0
Chats-Grid: An Iterative Retrieval Q&A Optimization Scheme Leveraging Large Model and Retrieval Enhancement Generation in smart grid—0
GeoCoder: Solving Geometry Problems by Generating Modular Code through Vision-Language Models—0
GenDFIR: Advancing Cyber Incident Timeline Analysis Through Retrieval Augmented Generation and Large Language Models—0
Chinese SafetyQA: A Safety Short-form Factuality Benchmark for Large Language Models—0
FinBERT2: A Specialized Bidirectional Encoder for Bridging the Gap in Finance-Specific Deployment of Large Language Models—0
Augmenting Large Language Models with Static Code Analysis for Automated Code Quality Improvements—0
FIND: Fine-grained Information Density Guided Adaptive Retrieval-Augmented Generation for Disease Diagnosis—0
Evaluating and Enhancing Large Language Models Performance in Domain-specific Medicine: Osteoarthritis Management with DocOA—0
FineFilter: A Fine-grained Noise Filtering Mechanism for Retrieval-Augmented Large Language Models—0
CALLM: Understanding Cancer Survivors' Emotions and Intervention Opportunities via Mobile Diaries and Context-Aware Language Models—0
A Pilot Empirical Study on When and How to Use Knowledge Graphs as Retrieval Augmented Generation—0
Fine-Grained Retrieval-Augmented Generation for Visual Question Answering—0
GEM-RAG: Graphical Eigen Memories For Retrieval Augmented Generation—0
Establishing Performance Baselines in Fine-Tuning, Retrieval-Augmented Generation and Soft-Prompting for Non-Specialist LLM Users—0
ESGReveal: An LLM-based approach for extracting structured data from ESG reports—0
Fine-Tuning and Prompt Optimization: Two Great Steps that Work Better Together—0
Fine-Tuning Large Language Models and Evaluating Retrieval Methods for Improved Question Answering on Building Codes—0
Calibrated Decision-Making through LLM-Assisted Retrieval—0
ER-RAG: Enhance RAG with ER-Based Unified Modeling of Heterogeneous Data Sources—0
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