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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 1751–1800 of 2111 papers

TitleStatusHype
Query Routing for Retrieval-Augmented Language Models—0
Question-Answering Based Summarization of Electronic Health Records using Retrieval Augmented Generation—0
Question-Based Retrieval using Atomic Units for Enterprise RAG—0
QuIM-RAG: Advancing Retrieval-Augmented Generation with Inverted Question Matching for Enhanced QA Performance—0
QuOTE: Question-Oriented Text Embeddings—0
Qwen2.5-32B: Leveraging Self-Consistent Tool-Integrated Reasoning for Bengali Mathematical Olympiad Problem Solving—0
QwenLong-CPRS: Towards -LLMs with Dynamic Context Optimization—0
R^3AG: First Workshop on Refined and Reliable Retrieval Augmented Generation—0
R3 : Refined Retriever-Reader pipeline for Multidoc2dial—0
RAAD-LLM: Adaptive Anomaly Detection Using LLMs and RAG Integration—0
RAC3: Retrieval-Augmented Corner Case Comprehension for Autonomous Driving with Vision-Language Models—0
RAD: Retrieval-Augmented Decision-Making of Meta-Actions with Vision-Language Models in Autonomous Driving—0
RaFe: Ranking Feedback Improves Query Rewriting for RAG—0
RAG4ITOps: A Supervised Fine-Tunable and Comprehensive RAG Framework for IT Operations and Maintenance—0
RAG-6DPose: Retrieval-Augmented 6D Pose Estimation via Leveraging CAD as Knowledge Base—0
Rag and Roll: An End-to-End Evaluation of Indirect Prompt Manipulations in LLM-based Application Frameworks—0
RAGAR, Your Falsehood Radar: RAG-Augmented Reasoning for Political Fact-Checking using Multimodal Large Language Models—0
RAG-based Crowdsourcing Task Decomposition via Masked Contrastive Learning with Prompts—0
RAG-based Explainable Prediction of Road Users Behaviors for Automated Driving using Knowledge Graphs and Large Language Models—0
RAG based Question-Answering for Contextual Response Prediction System—0
RAG-based Question Answering over Heterogeneous Data and Text—0
RAG-based User Profiling for Precision Planning in Mixed-precision Over-the-Air Federated Learning—0
RAGBench: Explainable Benchmark for Retrieval-Augmented Generation Systems—0
RAGCache: Efficient Knowledge Caching for Retrieval-Augmented Generation—0
RAG-Check: Evaluating Multimodal Retrieval Augmented Generation Performance—0
RAGDiffusion: Faithful Cloth Generation via External Knowledge Assimilation—0
RAG Does Not Work for Enterprises—0
Natural Language Interaction with a Household Electricity Knowledge-based Digital Twin—0
RAG-Enhanced Collaborative LLM Agents for Drug Discovery—0
RAG+: Enhancing Retrieval-Augmented Generation with Application-Aware Reasoning—0
RAG for Effective Supply Chain Security Questionnaire Automation—0
RAG-Fusion: a New Take on Retrieval-Augmented Generation—0
RAGged Edges: The Double-Edged Sword of Retrieval-Augmented Chatbots—0
RAG-Gym: Optimizing Reasoning and Search Agents with Process Supervision—0
RAG-KG-IL: A Multi-Agent Hybrid Framework for Reducing Hallucinations and Enhancing LLM Reasoning through RAG and Incremental Knowledge Graph Learning Integration—0
RAG/LLM Augmented Switching Driven Polymorphic Metaheuristic Framework—0
RAG LLMs are Not Safer: A Safety Analysis of Retrieval-Augmented Generation for Large Language Models—0
RAG-MCP: Mitigating Prompt Bloat in LLM Tool Selection via Retrieval-Augmented Generation—0
RAG-Modulo: Solving Sequential Tasks using Experience, Critics, and Language Models—0
RAGONITE: Iterative Retrieval on Induced Databases and Verbalized RDF for Conversational QA over KGs with RAG—0
RAG-Optimized Tibetan Tourism LLMs: Enhancing Accuracy and Personalization—0
RAG-Reward: Optimizing RAG with Reward Modeling and RLHF—0
RAG-RL: Advancing Retrieval-Augmented Generation via RL and Curriculum Learning—0
RAGServe: Fast Quality-Aware RAG Systems with Configuration Adaptation—0
RAG-Star: Enhancing Deliberative Reasoning with Retrieval Augmented Verification and Refinement—0
RAGSys: Item-Cold-Start Recommender as RAG System—0
RAGtifier: Evaluating RAG Generation Approaches of State-of-the-Art RAG Systems for the SIGIR LiveRAG Competition—0
RAGulator: Lightweight Out-of-Context Detectors for Grounded Text Generation—0
RAG-Verus: Repository-Level Program Verification with LLMs using Retrieval Augmented Generation—0
RAG vs Fine-tuning: Pipelines, Tradeoffs, and a Case Study on Agriculture—0
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