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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 701–725 of 2111 papers

TitleStatusHype
Towards Artificial General or Personalized Intelligence? A Survey on Foundation Models for Personalized Federated Intelligence—0
ThreatLens: LLM-guided Threat Modeling and Test Plan Generation for Hardware Security Verification—0
OMGM: Orchestrate Multiple Granularities and Modalities for Efficient Multimodal Retrieval—0
System Prompt Poisoning: Persistent Attacks on Large Language Models Beyond User Injection—0
NeoQA: Evidence-based Question Answering with Generated News EventsCode0
ArtRAG: Retrieval-Augmented Generation with Structured Context for Visual Art Understanding—0
Multimodal Integrated Knowledge Transfer to Large Language Models through Preference Optimization with Biomedical ApplicationsCode0
AI Approaches to Qualitative and Quantitative News Analytics on NATO Unity—0
LSRP: A Leader-Subordinate Retrieval Framework for Privacy-Preserving Cloud-Device CollaborationCode0
Stealthy LLM-Driven Data Poisoning Attacks Against Embedding-Based Retrieval-Augmented Recommender Systems—0
An Open-Source Dual-Loss Embedding Model for Semantic Retrieval in Higher Education—0
VR-RAG: Open-vocabulary Species Recognition with RAG-Assisted Large Multi-Modal Models—0
Lost in OCR Translation? Vision-Based Approaches to Robust Document Retrieval—0
Defending against Indirect Prompt Injection by Instruction DetectionCode0
QualBench: Benchmarking Chinese LLMs with Localized Professional Qualifications for Vertical Domain Evaluation—0
Fine-Tuning Large Language Models and Evaluating Retrieval Methods for Improved Question Answering on Building Codes—0
HiPerRAG: High-Performance Retrieval Augmented Generation for Scientific Insights—0
A Proposal for Evaluating the Operational Risk for ChatBots based on Large Language Models—0
LLM-Independent Adaptive RAG: Let the Question Speak for Itself—0
Retrieval Augmented Generation Evaluation for Health Documents—0
Osiris: A Lightweight Open-Source Hallucination Detection System—0
The Aloe Family Recipe for Open and Specialized Healthcare LLMs—0
RAG-MCP: Mitigating Prompt Bloat in LLM Tool Selection via Retrieval-Augmented Generation—0
A Reasoning-Focused Legal Retrieval Benchmark—0
Capability-Driven Skill Generation with LLMs: A RAG-Based Approach for Reusing Existing Libraries and Interfaces—0
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