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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 1051–1100 of 2111 papers

TitleStatusHype
Enhancing Cross-Language Code Translation via Task-Specific Embedding Alignment in Retrieval-Augmented Generation—0
QueEn: A Large Language Model for Quechua-English Translation—0
SurgBox: Agent-Driven Operating Room Sandbox with Surgery CopilotCode1
Privacy-Preserving Retrieval-Augmented Generation with Differential Privacy—0
Leveraging Large Language Models to Generate Course-specific Semantically Annotated Learning Objects—0
Targeting the Core: A Simple and Effective Method to Attack RAG-based Agents via Direct LLM Manipulation—0
Retrieval-Augmented Machine Translation with Unstructured KnowledgeCode1
HEAL: Hierarchical Embedding Alignment Loss for Improved Retrieval and Representation LearningCode1
Exploring AI Text Generation, Retrieval-Augmented Generation, and Detection Technologies: a Comprehensive Overview—0
Addressing Hallucinations with RAG and NMISS in Italian Healthcare LLM Chatbots—0
Advancing Conversational Psychotherapy: Integrating Privacy, Dual-Memory, and Domain Expertise with Large Language Models—0
A Review on Scientific Knowledge Extraction using Large Language Models in Biomedical Sciences—0
Hybrid-SQuAD: Hybrid Scholarly Question Answering Dataset—0
CAISSON: Concept-Augmented Inference Suite of Self-Organizing Neural Networks—0
Leveraging Large Language Models to Democratize Access to Costly Datasets for Academic Research—0
Composing Open-domain Vision with RAG for Ocean Monitoring and Conservation—0
OCR Hinders RAG: Evaluating the Cascading Impact of OCR on Retrieval-Augmented GenerationCode2
Semantic Tokens in Retrieval Augmented Generation—0
Query Performance Explanation through Large Language Model for HTAP Systems—0
Medchain: Bridging the Gap Between LLM Agents and Clinical Practice through Interactive Sequential Benchmarking—0
MBA-RAG: a Bandit Approach for Adaptive Retrieval-Augmented Generation through Question ComplexityCode1
Improving Multimodal LLMs Ability In Geometry Problem Solving, Reasoning, And Multistep Scoring—0
Rethinking Strategic Mechanism Design In The Age Of Large Language Models: New Directions For Communication Systems—0
Leveraging LLM for Automated Ontology Extraction and Knowledge Graph Generation—0
Generating a Low-code Complete Workflow via Task Decomposition and RAG—0
Auto-RAG: Autonomous Retrieval-Augmented Generation for Large Language ModelsCode3
Know Your RAG: Dataset Taxonomy and Generation Strategies for Evaluating RAG Systems—0
Unimib Assistant: designing a student-friendly RAG-based chatbot for all their needs—0
Advanced System Integration: Analyzing OpenAPI Chunking for Retrieval-Augmented Generation—0
Knowledge Management for Automobile Failure Analysis Using Graph RAG—0
Towards Understanding Retrieval Accuracy and Prompt Quality in RAG Systems—0
RAGDiffusion: Faithful Cloth Generation via External Knowledge Assimilation—0
SIMS: Simulating Stylized Human-Scene Interactions with Retrieval-Augmented Script Generation—0
Efficient Learning Content Retrieval with Knowledge Injection—0
ICLERB: In-Context Learning Embedding and Reranker Benchmark—0
RevPRAG: Revealing Poisoning Attacks in Retrieval-Augmented Generation through LLM Activation Analysis—0
Habit Coach: Customising RAG-based chatbots to support behavior change—0
Evaluating and Improving the Robustness of Security Attack Detectors Generated by LLMsCode0
Automated Literature Review Using NLP Techniques and LLM-Based Retrieval-Augmented Generation—0
Path-RAG: Knowledge-Guided Key Region Retrieval for Open-ended Pathology Visual Question AnsweringCode2
AtomR: Atomic Operator-Empowered Large Language Models for Heterogeneous Knowledge ReasoningCode1
Context Awareness Gate For Retrieval Augmented GenerationCode1
Multi-modal Retrieval Augmented Multi-modal Generation: A Benchmark, Evaluate Metrics and Strong BaselinesCode1
LaB-RAG: Label Boosted Retrieval Augmented Generation for Radiology Report GenerationCode1
Human-Calibrated Automated Testing and Validation of Generative Language Models—0
RAMIE: Retrieval-Augmented Multi-task Information Extraction with Large Language Models on Dietary Supplements—0
Multi-Reranker: Maximizing performance of retrieval-augmented generation in the FinanceRAG challengeCode2
Document Haystacks: Vision-Language Reasoning Over Piles of 1000+ DocumentsCode0
Traditional Chinese Medicine Case Analysis System for High-Level Semantic Abstraction: Optimized with Prompt and RAG—0
From MTEB to MTOB: Retrieval-Augmented Classification for Descriptive GrammarsCode0
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