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 851–900 of 2111 papers

TitleStatusHype
RAG-Reward: Optimizing RAG with Reward Modeling and RLHF—0
EvidenceMap: Learning Evidence Analysis to Unleash the Power of Small Language Models for Biomedical Question Answering—0
Adaptive Retrieval Without Self-Knowledge? Bringing Uncertainty Back Home—0
Leveraging Large Language Models to Enhance Machine Learning Interpretability and Predictive Performance: A Case Study on Emergency Department Returns for Mental Health Patients—0
A Survey of Graph Retrieval-Augmented Generation for Customized Large Language ModelsCode7
Med-R^2: Crafting Trustworthy LLM Physicians via Retrieval and Reasoning of Evidence-Based MedicineCode1
ALoFTRAG: Automatic Local Fine Tuning for Retrieval Augmented GenerationCode0
Network-informed Prompt Engineering against Organized Astroturf Campaigns under Extreme Class ImbalanceCode0
TutorLLM: Customizing Learning Recommendations with Knowledge Tracing and Retrieval-Augmented Generation—0
Zep: A Temporal Knowledge Graph Architecture for Agent MemoryCode12
Chat3GPP: An Open-Source Retrieval-Augmented Generation Framework for 3GPP DocumentsCode1
ImageRef-VL: Enabling Contextual Image Referencing in Vision-Language ModelsCode0
Poison-RAG: Adversarial Data Poisoning Attacks on Retrieval-Augmented Generation in Recommender SystemsCode0
Explainable Lane Change Prediction for Near-Crash Scenarios Using Knowledge Graph Embeddings and Retrieval Augmented Generation—0
RACCOON: A Retrieval-Augmented Generation Approach for Location Coordinate Capture from News ArticlesCode0
PIKE-RAG: sPecIalized KnowledgE and Rationale Augmented GenerationCode7
InsQABench: Benchmarking Chinese Insurance Domain Question Answering with Large Language ModelsCode1
GEC-RAG: Improving Generative Error Correction via Retrieval-Augmented Generation for Automatic Speech Recognition Systems—0
Learn-by-interact: A Data-Centric Framework for Self-Adaptive Agents in Realistic Environments—0
Visual RAG: Expanding MLLM visual knowledge without fine-tuning—0
4bit-Quantization in Vector-Embedding for RAGCode0
FRAG: A Flexible Modular Framework for Retrieval-Augmented Generation based on Knowledge Graphs—0
Passage Segmentation of Documents for Extractive Question Answering—0
AirRAG: Activating Intrinsic Reasoning for Retrieval Augmented Generation via Tree-based Search—0
Conversational Text Extraction with Large Language Models Using Retrieval-Augmented Systems—0
CyberMentor: AI Powered Learning Tool Platform to Address Diverse Student Needs in Cybersecurity EducationCode0
Agentic Retrieval-Augmented Generation: A Survey on Agentic RAGCode5
SteLLA: A Structured Grading System Using LLMs with RAG—0
ASTRID -- An Automated and Scalable TRIaD for the Evaluation of RAG-based Clinical Question Answering Systems—0
A Driver Advisory System Based on Large Language Model for High-speed Train—0
Engineering LLM Powered Multi-agent Framework for Autonomous CloudOps—0
Eliciting In-context Retrieval and Reasoning for Long-context Large Language Models—0
ReARTeR: Retrieval-Augmented Reasoning with Trustworthy Process RewardingCode4
ADAM-1: AI and Bioinformatics for Alzheimer's Detection and Microbiome-Clinical Data Integrations—0
Talk to Right Specialists: Routing and Planning in Multi-agent System for Question Answering—0
Research on the Online Update Method for Retrieval-Augmented Generation (RAG) Model with Incremental Learning—0
Enhancing Talent Employment Insights Through Feature Extraction with LLM Finetuning—0
Parallel Key-Value Cache Fusion for Position Invariant RAG—0
A Proposed Large Language Model-Based Smart Search for Archive System—0
LLM-Net: Democratizing LLMs-as-a-Service through Blockchain-based Expert Networks—0
Enhancing Retrieval-Augmented Generation: A Study of Best PracticesCode2
WebWalker: Benchmarking LLMs in Web TraversalCode11
Eliza: A Web3 friendly AI Agent Operating SystemCode11
MiniRAG: Towards Extremely Simple Retrieval-Augmented GenerationCode5
First Token Probability Guided RAG for Telecom Question Answering—0
BioAgents: Democratizing Bioinformatics Analysis with Multi-Agent Systems—0
VideoRAG: Retrieval-Augmented Generation over Video CorpusCode2
LLMQuoter: Enhancing RAG Capabilities Through Efficient Quote Extraction From Large ContextsCode0
RAG-WM: An Efficient Black-Box Watermarking Approach for Retrieval-Augmented Generation of Large Language Models—0
A General Retrieval-Augmented Generation Framework for Multimodal Case-Based Reasoning Applications—0
Show:102550
← PrevPage 18 of 43Next →

No leaderboard results yet.