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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 1951–2000 of 2111 papers

TitleStatusHype
Towards Lighter and Robust Evaluation for Retrieval Augmented GenerationCode0
CIIR@LiveRAG 2025: Optimizing Multi-Agent Retrieval Augmented Generation through Self-TrainingCode0
DRAFT-ing Architectural Design Decisions using LLMsCode0
LaRA: Benchmarking Retrieval-Augmented Generation and Long-Context LLMs - No Silver Bullet for LC or RAG RoutingCode0
PROPHET: An Inferable Future Forecasting Benchmark with Causal Intervened Likelihood EstimationCode0
DO-RAG: A Domain-Specific QA Framework Using Knowledge Graph-Enhanced Retrieval-Augmented GenerationCode0
TableRAG: Million-Token Table Understanding with Language ModelsCode0
PsyLite Technical ReportCode0
Towards More Robust Retrieval-Augmented Generation: Evaluating RAG Under Adversarial Poisoning AttacksCode0
Knowledge and Aptitude Augmented Generation: Adaptive Multi-Turn Interaction in LLM SystemsCode0
Adaptations of AI models for querying the LandMatrix database in natural languageCode0
Talk2X -- An Open-Source Toolkit Facilitating Deployment of LLM-Powered Chatbots on the WebCode0
Knowledgeable-r1: Policy Optimization for Knowledge Exploration in Retrieval-Augmented GenerationCode0
Know3-RAG: A Knowledge-aware RAG Framework with Adaptive Retrieval, Generation, and FilteringCode0
Talk Before You Retrieve: Agent-Led Discussions for Better RAG in Medical QACode0
Do "New Snow Tablets" Contain Snow? Large Language Models Over-Rely on Names to Identify Ingredients of Chinese DrugsCode0
Quantifying the Robustness of Retrieval-Augmented Language Models Against Spurious Features in Grounding DataCode0
Unleashing Worms and Extracting Data: Escalating the Outcome of Attacks against RAG-based Inference in Scale and Severity Using JailbreakingCode0
Quebec Automobile Insurance Question-Answering With Retrieval-Augmented GenerationCode0
Does RAG Introduce Unfairness in LLMs? Evaluating Fairness in Retrieval-Augmented Generation SystemsCode0
Agentic Search Engine for Real-Time IoT DataCode0
Robust affine point matching via quadratic assignment on GrassmanniansCode0
Retrieval Augmented Generation using Engineering Design KnowledgeCode0
QMOS: Enhancing LLMs for Telecommunication with Question Masked loss and Option ShufflingCode0
A Methodology for Evaluating RAG Systems: A Case Study On Configuration Dependency ValidationCode0
Where is the answer? Investigating Positional Bias in Language Model Knowledge ExtractionCode0
AI-TA: Towards an Intelligent Question-Answer Teaching Assistant using Open-Source LLMsCode0
R-Search: Empowering LLM Reasoning with Search via Multi-Reward Reinforcement LearningCode0
Who's Who: Large Language Models Meet Knowledge Conflicts in PracticeCode0
QPaug: Question and Passage Augmentation for Open-Domain Question Answering of LLMsCode0
K-COMP: Retrieval-Augmented Medical Domain Question Answering With Knowledge-Injected CompressorCode0
KBAlign: Efficient Self Adaptation on Specific Knowledge BasesCode0
JMLR: Joint Medical LLM and Retrieval Training for Enhancing Reasoning and Professional Question Answering CapabilityCode0
CBM-RAG: Demonstrating Enhanced Interpretability in Radiology Report Generation with Multi-Agent RAG and Concept Bottleneck ModelsCode0
IRSC: A Zero-shot Evaluation Benchmark for Information Retrieval through Semantic Comprehension in Retrieval-Augmented Generation ScenariosCode0
RuCCoD: Towards Automated ICD Coding in RussianCode0
Does Context Matter? ContextualJudgeBench for Evaluating LLM-based Judges in Contextual SettingsCode0
Document Haystacks: Vision-Language Reasoning Over Piles of 1000+ DocumentsCode0
RACCOON: A Retrieval-Augmented Generation Approach for Location Coordinate Capture from News ArticlesCode0
RAC: Efficient LLM Factuality Correction with Retrieval AugmentationCode0
RAD-Bench: Evaluating Large Language Models Capabilities in Retrieval Augmented DialoguesCode0
RadioRAG: Factual large language models for enhanced diagnostics in radiology using online retrieval augmented generationCode0
RustEvo^2: An Evolving Benchmark for API Evolution in LLM-based Rust Code GenerationCode0
RAEmoLLM: Retrieval Augmented LLMs for Cross-Domain Misinformation Detection Using In-Context Learning based on Emotional InformationCode0
DNAHLM -- DNA sequence and Human Language mixed large language ModelCode0
RAFT: Adapting Language Model to Domain Specific RAGCode0
DIRAS: Efficient LLM Annotation of Document Relevance in Retrieval Augmented GenerationCode0
Walk&Retrieve: Simple Yet Effective Zero-shot Retrieval-Augmented Generation via Knowledge Graph WalksCode0
Investigating the performance of Retrieval-Augmented Generation and fine-tuning for the development of AI-driven knowledge-based systemsCode0
Developing Visual Augmented Q&A System using Scalable Vision Embedding Retrieval & Late Interaction Re-rankerCode0
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