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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 476–500 of 2111 papers

TitleStatusHype
The Dark Side of LLMs Agent-based Attacks for Complete Computer Takeover—0
SARA: Selective and Adaptive Retrieval-augmented Generation with Context Compression—0
Flippi: End To End GenAI Assistant for E-Commerce—0
Evaluating Memory in LLM Agents via Incremental Multi-Turn Interactions—0
AI-VaxGuide: An Agentic RAG-Based LLM for Vaccination Decisions—0
Knowledge Protocol Engineering: A New Paradigm for AI in Domain-Specific Knowledge Work—0
CyberRAG: An agentic RAG cyber attack classification and reporting tool—0
Knowledge Augmented Finetuning Matters in both RAG and Agent Based Dialog Systems—0
ARAG: Agentic Retrieval Augmented Generation for Personalized Recommendation—0
Leveraging LLM-Assisted Query Understanding for Live Retrieval-Augmented Generation—0
PsyLite Technical ReportCode0
Response Quality Assessment for Retrieval-Augmented Generation via Conditional Conformal FactualityCode0
Fine-Tuning and Prompt Engineering of LLMs, for the Creation of Multi-Agent AI for Addressing Sustainable Protein Production ChallengesCode0
AI Assistants to Enhance and Exploit the PETSc Knowledge Base—0
Engineering RAG Systems for Real-World Applications: Design, Development, and Evaluation—0
RAG-VisualRec: An Open Resource for Vision- and Text-Enhanced Retrieval-Augmented Generation in RecommendationCode0
CCRS: A Zero-Shot LLM-as-a-Judge Framework for Comprehensive RAG Evaluation—0
MultiFinRAG: An Optimized Multimodal Retrieval-Augmented Generation (RAG) Framework for Financial Question Answering—0
SV-LLM: An Agentic Approach for SoC Security Verification using Large Language Models—0
Knowledge-Aware Diverse Reranking for Cross-Source Question Answering—0
Memento: Note-Taking for Your Future Self—0
Controlled Retrieval-augmented Context Evaluation for Long-form RAG—0
Accurate and Energy Efficient: Local Retrieval-Augmented Generation Models Outperform Commercial Large Language Models in Medical Tasks—0
Dialogic Pedagogy for Large Language Models: Aligning Conversational AI with Proven Theories of Learning—0
QHackBench: Benchmarking Large Language Models for Quantum Code Generation Using PennyLane Hackathon Challenges—0
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