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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 526–550 of 2111 papers

TitleStatusHype
Efficient Context Selection for Long-Context QA: No Tuning, No Iteration, Just Adaptive-k—0
CC-RAG: Structured Multi-Hop Reasoning via Theme-Based Causal Graphs—0
Safeguarding Multimodal Knowledge Copyright in the RAG-as-a-Service EnvironmentCode0
XGraphRAG: Interactive Visual Analysis for Graph-based Retrieval-Augmented GenerationCode0
Swiss Parliaments Corpus Re-Imagined (SPC_R): Enhanced Transcription with RAG-based Correction and Predicted BLEU—0
LlamaRec-LKG-RAG: A Single-Pass, Learnable Knowledge Graph-RAG Framework for LLM-Based RankingCode0
Knowledge Compression via Question Generation: Enhancing Multihop Document Retrieval without Fine-tuning—0
SceneRAG: Scene-level Retrieval-Augmented Generation for Video Understanding—0
LEANN: A Low-Storage Vector Index—0
Repeton: Structured Bug Repair with ReAct-Guided Patch-and-Test Cycles—0
AR-RAG: Autoregressive Retrieval Augmentation for Image GenerationCode0
BioMol-MQA: A Multi-Modal Question Answering Dataset For LLM Reasoning Over Bio-Molecular Interactions—0
Small Models, Big Support: A Local LLM Framework for Teacher-Centric Content Creation and Assessment using RAG and CAG—0
From Standalone LLMs to Integrated Intelligence: A Survey of Compound Al Systems—0
Micro-Act: Mitigate Knowledge Conflict in Question Answering via Actionable Self-ReasoningCode0
Knowledgeable-r1: Policy Optimization for Knowledge Exploration in Retrieval-Augmented GenerationCode0
Dynamic Context Tuning for Retrieval-Augmented Generation: Enhancing Multi-Turn Planning and Tool Adaptation—0
On Automating Security Policies with Contemporary LLMs—0
Mathematical Reasoning for Unmanned Aerial Vehicles: A RAG-Based Approach for Complex Arithmetic ReasoningCode0
Magic Mushroom: A Customizable Benchmark for Fine-grained Analysis of Retrieval Noise Erosion in RAG Systems—0
GEM: Empowering LLM for both Embedding Generation and Language Understanding—0
Through the Stealth Lens: Rethinking Attacks and Defenses in RAGCode0
R-Search: Empowering LLM Reasoning with Search via Multi-Reward Reinforcement LearningCode0
CoRe-MMRAG: Cross-Source Knowledge Reconciliation for Multimodal RAG—0
MotionRAG-Diff: A Retrieval-Augmented Diffusion Framework for Long-Term Music-to-Dance Generation—0
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