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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 701–750 of 2111 papers

TitleStatusHype
ThreatLens: LLM-guided Threat Modeling and Test Plan Generation for Hardware Security Verification—0
Towards Artificial General or Personalized Intelligence? A Survey on Foundation Models for Personalized Federated Intelligence—0
System Prompt Poisoning: Persistent Attacks on Large Language Models Beyond User Injection—0
OMGM: Orchestrate Multiple Granularities and Modalities for Efficient Multimodal Retrieval—0
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
NeoQA: Evidence-based Question Answering with Generated News EventsCode0
AI Approaches to Qualitative and Quantitative News Analytics on NATO Unity—0
VR-RAG: Open-vocabulary Species Recognition with RAG-Assisted Large Multi-Modal Models—0
QualBench: Benchmarking Chinese LLMs with Localized Professional Qualifications for Vertical Domain Evaluation—0
LSRP: A Leader-Subordinate Retrieval Framework for Privacy-Preserving Cloud-Device CollaborationCode0
Lost in OCR Translation? Vision-Based Approaches to Robust Document Retrieval—0
An Open-Source Dual-Loss Embedding Model for Semantic Retrieval in Higher Education—0
Stealthy LLM-Driven Data Poisoning Attacks Against Embedding-Based Retrieval-Augmented Recommender Systems—0
Defending against Indirect Prompt Injection by Instruction DetectionCode0
LLM-Independent Adaptive RAG: Let the Question Speak for Itself—0
HiPerRAG: High-Performance Retrieval Augmented Generation for Scientific Insights—0
The Aloe Family Recipe for Open and Specialized Healthcare LLMs—0
Osiris: A Lightweight Open-Source Hallucination Detection System—0
Retrieval Augmented Generation Evaluation for Health Documents—0
A Proposal for Evaluating the Operational Risk for ChatBots based on Large Language Models—0
Fine-Tuning Large Language Models and Evaluating Retrieval Methods for Improved Question Answering on Building Codes—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
Lightweight Clinical Decision Support System using QLoRA-Fine-Tuned LLMs and Retrieval-Augmented Generation—0
An Analysis of Hyper-Parameter Optimization Methods for Retrieval Augmented Generation—0
SafeMate: A Modular RAG-Based Agent for Context-Aware Emergency Guidance—0
SymbioticRAG: Enhancing Document Intelligence Through Human-LLM Symbiotic Collaboration—0
Generative Sign-description Prompts with Multi-positive Contrastive Learning for Sign Language Recognition—0
Retrieval-augmented in-context learning for multimodal large language models in disease classification—0
Real-time Spatial Retrieval Augmented Generation for Urban Environments—0
Incorporating Legal Structure in Retrieval-Augmented Generation: A Case Study on Copyright Fair UseCode0
A New HOPE: Domain-agnostic Automatic Evaluation of Text Chunking—0
Retrieval-Augmented Generation in Biomedicine: A Survey of Technologies, Datasets, and Clinical Applications—0
CHORUS: Zero-shot Hierarchical Retrieval and Orchestration for Generating Linear Programming Code—0
Facilitating Video Story Interaction with Multi-Agent Collaborative System—0
Enhancing tutoring systems by leveraging tailored promptings and domain knowledge with Large Language Models—0
Harnessing Structured Knowledge: A Concept Map-Based Approach for High-Quality Multiple Choice Question Generation with Effective DistractorsCode0
Document Retrieval Augmented Fine-Tuning (DRAFT) for safety-critical software assessments—0
Patchwork: A Unified Framework for RAG Serving—0
HalluMix: A Task-Agnostic, Multi-Domain Benchmark for Real-World Hallucination Detection—0
EnronQA: Towards Personalized RAG over Private Documents—0
Empowering Agentic Video Analytics Systems with Video Language Models—0
A Multi-Granularity Retrieval Framework for Visually-Rich Documents—0
Homa at SemEval-2025 Task 5: Aligning Librarian Records with OntoAligner for Subject Tagging—0
Traceback of Poisoning Attacks to Retrieval-Augmented Generation—0
Optimization of embeddings storage for RAG systems using quantization and dimensionality reduction techniques—0
Talk Before You Retrieve: Agent-Led Discussions for Better RAG in Medical QACode0
ARCS: Agentic Retrieval-Augmented Code Synthesis with Iterative Refinement—0
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