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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 501–525 of 2111 papers

TitleStatusHype
Inference Scaled GraphRAG: Improving Multi Hop Question Answering on Knowledge Graphs—0
QHackBench: Benchmarking Large Language Models for Quantum Code Generation Using PennyLane Hackathon Challenges—0
T-CPDL: A Temporal Causal Probabilistic Description Logic for Developing Logic-RAG Agent—0
RAG-6DPose: Retrieval-Augmented 6D Pose Estimation via Leveraging CAD as Knowledge Base—0
REIS: A High-Performance and Energy-Efficient Retrieval System with In-Storage Processing—0
From RAG to Agentic: Validating Islamic-Medicine Responses with LLM Agents—0
SciVer: Evaluating Foundation Models for Multimodal Scientific Claim Verification—0
Lightweight Relevance Grader in RAGCode0
RAGtifier: Evaluating RAG Generation Approaches of State-of-the-Art RAG Systems for the SIGIR LiveRAG Competition—0
AviationLLM: An LLM-based Knowledge System for Aviation Training—0
Automated Decision-Making on Networks with LLMs through Knowledge-Guided Evolution—0
Tree-Based Text Retrieval via Hierarchical Clustering in RAGFrameworks: Application on Taiwanese RegulationsCode0
LTRR: Learning To Rank Retrievers for LLMsCode0
AdaVideoRAG: Omni-Contextual Adaptive Retrieval-Augmented Efficient Long Video UnderstandingCode0
Chunk Twice, Embed Once: A Systematic Study of Segmentation and Representation Trade-offs in Chemistry-Aware Retrieval-Augmented Generation—0
RAG+: Enhancing Retrieval-Augmented Generation with Application-Aware Reasoning—0
Large Language Model-Powered Conversational Agent Delivering Problem-Solving Therapy (PST) for Family Caregivers: Enhancing Empathy and Therapeutic Alliance Using In-Context Learning—0
Bias Amplification in RAG: Poisoning Knowledge Retrieval to Steer LLMs—0
Dr. GPT Will See You Now, but Should It? Exploring the Benefits and Harms of Large Language Models in Medical Diagnosis using Crowdsourced Clinical Cases—0
Reasoning RAG via System 1 or System 2: A Survey on Reasoning Agentic Retrieval-Augmented Generation for Industry ChallengesCode0
CIIR@LiveRAG 2025: Optimizing Multi-Agent Retrieval Augmented Generation through Self-TrainingCode0
Augmenting Large Language Models with Static Code Analysis for Automated Code Quality Improvements—0
LLM Embedding-based Attribution (LEA): Quantifying Source Contributions to Generative Model's Response for Vulnerability AnalysisCode0
Learning Efficient and Generalizable Graph Retriever for Knowledge-Graph Question AnsweringCode0
Bridging the Gap Between Open-Source and Proprietary LLMs in Table QACode0
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