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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 601–650 of 2111 papers

TitleStatusHype
ASRank: Zero-Shot Re-Ranking with Answer Scent for Document Retrieval—0
CollEX -- A Multimodal Agentic RAG System Enabling Interactive Exploration of Scientific Collections—0
Collapse of Dense Retrievers: Short, Early, and Literal Biases Outranking Factual Evidence—0
A Socratic RAG Approach to Connect Natural Language Queries on Research Topics with Knowledge Organization Systems—0
Accurate and Energy Efficient: Local Retrieval-Augmented Generation Models Outperform Commercial Large Language Models in Medical Tasks—0
EvidenceMap: Learning Evidence Analysis to Unleash the Power of Small Language Models for Biomedical Question Answering—0
Cognitive-Aligned Document Selection for Retrieval-augmented Generation—0
Agentic Retrieval-Augmented Generation for Time Series Analysis—0
CodeXEmbed: A Generalist Embedding Model Family for Multiligual and Multi-task Code Retrieval—0
Ask-EDA: A Design Assistant Empowered by LLM, Hybrid RAG and Abbreviation De-hallucination—0
A Simple Architecture for Enterprise Large Language Model Applications based on Role based security and Clearance Levels using Retrieval-Augmented Generation or Mixture of Experts—0
Multi-Level Querying using A Knowledge Pyramid—0
EvoPat: A Multi-LLM-based Patents Summarization and Analysis Agent—0
ExpertRAG: Efficient RAG with Mixture of Experts -- Optimizing Context Retrieval for Adaptive LLM Responses—0
Code Graph Model (CGM): A Graph-Integrated Large Language Model for Repository-Level Software Engineering Tasks—0
Clustering Algorithms and RAG Enhancing Semi-Supervised Text Classification with Large LLMs—0
ArtRAG: Retrieval-Augmented Generation with Structured Context for Visual Art Understanding—0
Artificial Intelligence as the New Hacker: Developing Agents for Offensive Security—0
CL-RAG: Bridging the Gap in Retrieval-Augmented Generation with Curriculum Learning—0
Accelerating Retrieval-Augmented Generation—0
Evaluation of Semantic Search and its Role in Retrieved-Augmented-Generation (RAG) for Arabic Language—0
CLI-RAG: A Retrieval-Augmented Framework for Clinically Structured and Context Aware Text Generation with LLMs—0
Agentic Multimodal AI for Hyperpersonalized B2B and B2C Advertising in Competitive Markets: An AI-Driven Competitive Advertising Framework—0
Evaluation of RAG Metrics for Question Answering in the Telecom Domain—0
Faculty Perspectives on the Potential of RAG in Computer Science Higher Education—0
EventChat: Implementation and user-centric evaluation of a large language model-driven conversational recommender system for exploring leisure events in an SME context—0
Class-RAG: Real-Time Content Moderation with Retrieval Augmented Generation—0
Classifying Peace in Global Media Using RAG and Intergroup Reciprocity—0
ARise: Towards Knowledge-Augmented Reasoning via Risk-Adaptive Search—0
Claim Verification in the Age of Large Language Models: A Survey—0
A Review on Scientific Knowledge Extraction using Large Language Models in Biomedical Sciences—0
Agentic AI-Driven Technical Troubleshooting for Enterprise Systems: A Novel Weighted Retrieval-Augmented Generation Paradigm—0
ClaimTrust: Propagation Trust Scoring for RAG Systems—0
A review of faithfulness metrics for hallucination assessment in Large Language Models—0
CiteFix: Enhancing RAG Accuracy Through Post-Processing Citation Correction—0
A Retrieval-Augmented Generation Framework for Academic Literature Navigation in Data Science—0
Accelerating Manufacturing Scale-Up from Material Discovery Using Agentic Web Navigation and Retrieval-Augmented AI for Process Engineering Schematics Design—0
Evaluation of Attribution Bias in Retrieval-Augmented Large Language Models—0
Everything Can Be Described in Words: A Simple Unified Multi-Modal Framework with Semantic and Temporal Alignment—0
Are LLMs Prescient? A Continuous Evaluation using Daily News as the Oracle—0
Evaluating the Performance of RAG Methods for Conversational AI in the Airport Domain—0
Chunk Twice, Embed Once: A Systematic Study of Segmentation and Representation Trade-offs in Chemistry-Aware Retrieval-Augmented Generation—0
ChunkRAG: Novel LLM-Chunk Filtering Method for RAG Systems—0
Evaluating the Retrieval Component in LLM-Based Question Answering Systems—0
CHORUS: Zero-shot Hierarchical Retrieval and Orchestration for Generating Linear Programming Code—0
A General Retrieval-Augmented Generation Framework for Multimodal Case-Based Reasoning Applications—0
Chinese SafetyQA: A Safety Short-form Factuality Benchmark for Large Language Models—0
A Reliable Knowledge Processing Framework for Combustion Science using Foundation Models—0
Accelerating Inference of Retrieval-Augmented Generation via Sparse Context Selection—0
Evaluating Transferability in Retrieval Tasks: An Approach Using MMD and Kernel Methods—0
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