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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 1051–1075 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
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