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 501–550 of 2111 papers

TitleStatusHype
Imagine All The Relevance: Scenario-Profiled Indexing with Knowledge Expansion for Dense RetrievalCode1
Citegeist: Automated Generation of Related Work Analysis on the arXiv CorpusCode0
Memory-Aware and Uncertainty-Guided Retrieval for Multi-Hop Question Answering—0
DAT: Dynamic Alpha Tuning for Hybrid Retrieval in Retrieval-Augmented Generation—0
Understanding Inequality of LLM Fact-Checking over Geographic Regions with Agent and Retrieval models—0
MemInsight: Autonomous Memory Augmentation for LLM Agents—0
AutoPsyC: Automatic Recognition of Psychodynamic Conflicts from Semi-structured Interviews with Large Language Models—0
Real-Time Evaluation Models for RAG: Who Detects Hallucinations Best?—0
Tricking Retrievers with Influential Tokens: An Efficient Black-Box Corpus Poisoning Attack—0
ReaRAG: Knowledge-guided Reasoning Enhances Factuality of Large Reasoning Models with Iterative Retrieval Augmented GenerationCode1
HyperGraphRAG: Retrieval-Augmented Generation with Hypergraph-Structured Knowledge RepresentationCode3
A Survey of Multimodal Retrieval-Augmented Generation—0
Reasoning Beyond Limits: Advances and Open Problems for LLMs—0
MCTS-RAG: Enhancing Retrieval-Augmented Generation with Monte Carlo Tree SearchCode2
RALLRec+: Retrieval Augmented Large Language Model Recommendation with ReasoningCode0
Dewey Long Context Embedding Model: A Technical Report—0
RGL: A Graph-Centric, Modular Framework for Efficient Retrieval-Augmented Generation on GraphsCode2
CausalRAG: Integrating Causal Graphs into Retrieval-Augmented Generation—0
GridMind: A Multi-Agent NLP Framework for Unified, Cross-Modal NFL Data Insights—0
Improving RAG for Personalization with Author Features and Contrastive ExamplesCode0
Synthetic Function Demonstrations Improve Generation in Low-Resource Programming Languages—0
Fact-checking AI-generated news reports: Can LLMs catch their own lies?—0
ExpertRAG: Efficient RAG with Mixture of Experts -- Optimizing Context Retrieval for Adaptive LLM Responses—0
Retrieval Augmented Generation and Understanding in Vision: A Survey and New OutlookCode3
MedPlan:A Two-Stage RAG-Based System for Personalized Medical Plan Generation—0
GINGER: Grounded Information Nugget-Based Generation of ResponsesCode0
Experience Retrieval-Augmentation with Electronic Health Records Enables Accurate Discharge QACode0
MedAgent-Pro: Towards Evidence-based Multi-modal Medical Diagnosis via Reasoning Agentic WorkflowCode2
RustEvo^2: An Evolving Benchmark for API Evolution in LLM-based Rust Code GenerationCode0
An LLM-Powered Clinical Calculator Chatbot Backed by Verifiable Clinical Calculators and their Metadata—0
Autonomous Radiotherapy Treatment Planning Using DOLA: A Privacy-Preserving, LLM-Based Optimization Agent—0
Financial Analysis: Intelligent Financial Data Analysis System Based on LLM-RAG—0
FutureGen: LLM-RAG Approach to Generate the Future Work of Scientific ArticleCode0
Parameters vs. Context: Fine-Grained Control of Knowledge Reliance in Language ModelsCode1
Tuning LLMs by RAG Principles: Towards LLM-native MemoryCode1
Towards Lighter and Robust Evaluation for Retrieval Augmented GenerationCode0
Typed-RAG: Type-aware Multi-Aspect Decomposition for Non-Factoid Question AnsweringCode0
Graph-Based Re-ranking: Emerging Techniques, Limitations, and Opportunities—0
Enhancing Pancreatic Cancer Staging with Large Language Models: The Role of Retrieval-Augmented Generation—0
Bias Evaluation and Mitigation in Retrieval-Augmented Medical Question-Answering Systems—0
Optimizing Retrieval Strategies for Financial Question Answering Documents in Retrieval-Augmented Generation SystemsCode1
When Pigs Get Sick: Multi-Agent AI for Swine Disease Detection—0
Does Context Matter? ContextualJudgeBench for Evaluating LLM-based Judges in Contextual SettingsCode0
RAG-based User Profiling for Precision Planning in Mixed-precision Over-the-Air Federated Learning—0
Enhancing LLM Generation with Knowledge Hypergraph for Evidence-Based Medicine—0
RAGO: Systematic Performance Optimization for Retrieval-Augmented Generation ServingCode1
JuDGE: Benchmarking Judgment Document Generation for Chinese Legal SystemCode1
MoK-RAG: Mixture of Knowledge Paths Enhanced Retrieval-Augmented Generation for Embodied AI Environments—0
From "Hallucination" to "Suture": Insights from Language Philosophy to Enhance Large Language Models—0
Good/Evil Reputation Judgment of Celebrities by LLMs via Retrieval Augmented Generation—0
Show:102550
← PrevPage 11 of 43Next →

No leaderboard results yet.