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 351–400 of 2111 papers

TitleStatusHype
Reconstructing Context: Evaluating Advanced Chunking Strategies for Retrieval-Augmented GenerationCode0
TreeHop: Generate and Filter Next Query Embeddings Efficiently for Multi-hop Question AnsweringCode1
Enhancing Speech-to-Speech Dialogue Modeling with End-to-End Retrieval-Augmented GenerationCode1
RAG LLMs are Not Safer: A Safety Analysis of Retrieval-Augmented Generation for Large Language Models—0
A model and package for German ColBERT—0
SMARTFinRAG: Interactive Modularized Financial RAG BenchmarkCode0
LLMpatronous: Harnessing the Power of LLMs For Vulnerability Detection—0
A RAG-Based Multi-Agent LLM System for Natural Hazard Resilience and AdaptationCode1
Grounded in Context: Retrieval-Based Method for Hallucination Detection—0
FinDER: Financial Dataset for Question Answering and Evaluating Retrieval-Augmented Generation—0
Synergizing RAG and Reasoning: A Systematic Review—0
CiteFix: Enhancing RAG Accuracy Through Post-Processing Citation Correction—0
The Viability of Crowdsourcing for RAG EvaluationCode0
The Great Nugget Recall: Automating Fact Extraction and RAG Evaluation with Large Language Models—0
Retrieval Augmented Generation Evaluation in the Era of Large Language Models: A Comprehensive SurveyCode2
Efficient Document Retrieval with G-RetrieverCode0
LLMs as Data Annotators: How Close Are We to Human Performance—0
POLYRAG: Integrating Polyviews into Retrieval-Augmented Generation for Medical Applications—0
Support Evaluation for the TREC 2024 RAG Track: Comparing Human versus LLM Judges—0
AlignRAG: Leveraging Critique Learning for Evidence-Sensitive Retrieval-Augmented ReasoningCode1
ResNetVLLM-2: Addressing ResNetVLLM's Multi-Modal Hallucinations—0
FinSage: A Multi-aspect RAG System for Financial Filings Question Answering—0
LegalRAG: A Hybrid RAG System for Multilingual Legal Information Retrieval—0
SCRAG: Social Computing-Based Retrieval Augmented Generation for Community Response Forecasting in Social Media Environments—0
Fashion-RAG: Multimodal Fashion Image Editing via Retrieval-Augmented Generation—0
Secure Multifaceted-RAG for Enterprise: Hybrid Knowledge Retrieval with Security Filtering—0
RAG Without the Lag: Interactive Debugging for Retrieval-Augmented Generation Pipelines—0
CoT-RAG: Integrating Chain of Thought and Retrieval-Augmented Generation to Enhance Reasoning in Large Language Models—0
ACoRN: Noise-Robust Abstractive Compression in Retrieval-Augmented Language Models—0
Accommodate Knowledge Conflicts in Retrieval-augmented LLMs: Towards Reliable Response Generation in the Wild—0
FreshStack: Building Realistic Benchmarks for Evaluating Retrieval on Technical Documents—0
CDF-RAG: Causal Dynamic Feedback for Adaptive Retrieval-Augmented GenerationCode1
InstructRAG: Leveraging Retrieval-Augmented Generation on Instruction Graphs for LLM-Based Task Planning—0
Retrieval-Augmented Generation with Conflicting EvidenceCode1
Estimating Optimal Context Length for Hybrid Retrieval-augmented Multi-document SummarizationCode0
A Visual RAG Pipeline for Few-Shot Fine-Grained Product Classification—0
A Human-AI Comparative Analysis of Prompt Sensitivity in LLM-Based Relevance JudgmentCode0
On the Feasibility of Using MultiModal LLMs to Execute AR Social Engineering Attacks—0
ARCeR: an Agentic RAG for the Automated Definition of Cyber Ranges—0
Towards Conversational AI for Human-Machine Collaborative MLOps—0
Enhancing Autonomous Driving Systems with On-Board Deployed Large Language ModelsCode2
Timing Analysis Agent: Autonomous Multi-Corner Multi-Mode (MCMM) Timing Debugging with Timing Debug Relation Graph—0
LayoutCoT: Unleashing the Deep Reasoning Potential of Large Language Models for Layout Generation—0
ARise: Towards Knowledge-Augmented Reasoning via Risk-Adaptive Search—0
Towards Automated Safety Requirements Derivation Using Agent-based RAG—0
ReZero: Enhancing LLM search ability by trying one-more-time—0
Exploring the Role of Knowledge Graph-Based RAG in Japanese Medical Question Answering with Small-Scale LLMs—0
Efficient Distributed Retrieval-Augmented Generation for Enhancing Language Model Performance—0
VDocRAG: Retrieval-Augmented Generation over Visually-Rich Documents—0
RAKG:Document-level Retrieval Augmented Knowledge Graph ConstructionCode3
Show:102550
← PrevPage 8 of 43Next →

No leaderboard results yet.