SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 251–300 of 2111 papers

TitleStatusHype
Leveraging Graph Retrieval-Augmented Generation to Support Learners' Understanding of Knowledge Concepts in MOOCs—0
XRAG: Cross-lingual Retrieval-Augmented Generation—0
Hierarchical Document Refinement for Long-context Retrieval-augmented GenerationCode1
MMLongBench: Benchmarking Long-Context Vision-Language Models Effectively and ThoroughlyCode2
CL-RAG: Bridging the Gap in Retrieval-Augmented Generation with Curriculum Learning—0
CAFE: Retrieval Head-based Coarse-to-Fine Information Seeking to Enhance Multi-Document QA Capability—0
The Impact of Large Language Models on Task Automation in Manufacturing Services—0
Towards Automated Situation Awareness: A RAG-Based Framework for Peacebuilding Reports—0
Do Large Language Models Know Conflict? Investigating Parametric vs. Non-Parametric Knowledge of LLMs for Conflict Forecasting—0
A Multimodal Multi-Agent Framework for Radiology Report Generation—0
CXMArena: Unified Dataset to benchmark performance in realistic CXM ScenariosCode0
Improving the Reliability of LLMs: Combining CoT, RAG, Self-Consistency, and Self-Verification—0
Enhancing Cache-Augmented Generation (CAG) with Adaptive Contextual Compression for Scalable Knowledge Integration—0
Enhancing Thyroid Cytology Diagnosis with RAG-Optimized LLMs and Pa-thology Foundation Models—0
Securing RAG: A Risk Assessment and Mitigation Framework—0
WixQA: A Multi-Dataset Benchmark for Enterprise Retrieval-Augmented Generation—0
IterKey: Iterative Keyword Generation with LLMs for Enhanced Retrieval Augmented Generation—0
Scaling Context, Not Parameters: Training a Compact 7B Language Model for Efficient Long-Context Processing—0
Optimizing Retrieval-Augmented Generation: Analysis of Hyperparameter Impact on Performance and Efficiency—0
Aitomia: Your Intelligent Assistant for AI-Driven Atomistic and Quantum Chemical Simulations—0
Hakim: Farsi Text Embedding Model—0
MedEIR: A Specialized Medical Embedding Model for Enhanced Information Retrieval—0
KAQG: A Knowledge-Graph-Enhanced RAG for Difficulty-Controlled Question Generation—0
Why Uncertainty Estimation Methods Fall Short in RAG: An Axiomatic Analysis—0
Efficient and Reproducible Biomedical Question Answering using Retrieval Augmented GenerationCode1
SEReDeEP: Hallucination Detection in Retrieval-Augmented Models via Semantic Entropy and Context-Parameter Fusion—0
Chronocept: Instilling a Sense of Time in MachinesCode1
DynamicRAG: Leveraging Outputs of Large Language Model as Feedback for Dynamic Reranking in Retrieval-Augmented GenerationCode2
GRADA: Graph-based Reranker against Adversarial Documents AttackCode0
OnPrem.LLM: A Privacy-Conscious Document Intelligence ToolkitCode4
Reinforced Internal-External Knowledge Synergistic Reasoning for Efficient Adaptive Search AgentCode2
Towards Requirements Engineering for RAG Systems—0
Benchmarking Retrieval-Augmented Generation for Chemistry—0
The Distracting Effect: Understanding Irrelevant Passages in RAG—0
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
OMGM: Orchestrate Multiple Granularities and Modalities for Efficient Multimodal Retrieval—0
MacRAG: Compress, Slice, and Scale-up for Multi-Scale Adaptive Context RAGCode1
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
Defending against Indirect Prompt Injection by Instruction DetectionCode0
KG-HTC: Integrating Knowledge Graphs into LLMs for Effective Zero-shot Hierarchical Text ClassificationCode1
LSRP: A Leader-Subordinate Retrieval Framework for Privacy-Preserving Cloud-Device CollaborationCode0
Lost in OCR Translation? Vision-Based Approaches to Robust Document Retrieval—0
Stealthy LLM-Driven Data Poisoning Attacks Against Embedding-Based Retrieval-Augmented Recommender Systems—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
Show:102550
← PrevPage 6 of 43Next →

No leaderboard results yet.