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 751–800 of 2111 papers

TitleStatusHype
NeuroXVocal: Detection and Explanation of Alzheimer's Disease through Non-invasive Analysis of Picture-prompted Speech—0
KET-RAG: A Cost-Efficient Multi-Granular Indexing Framework for Graph-RAGCode2
Enhancing RAG with Active Learning on Conversation Records: Reject Incapables and Answer Capables—0
SQuARE: Sequential Question Answering Reasoning Engine for Enhanced Chain-of-Thought in Large Language ModelsCode4
Diversity Enhances an LLM's Performance in RAG and Long-context Task—0
Improving TCM Question Answering through Tree-Organized Self-Reflective Retrieval with LLMs—0
KIMAs: A Configurable Knowledge Integrated Multi-Agent System—0
ImageRAG: Dynamic Image Retrieval for Reference-Guided Image Generation—0
ParetoRAG: Leveraging Sentence-Context Attention for Robust and Efficient Retrieval-Augmented Generation—0
Ask in Any Modality: A Comprehensive Survey on Multimodal Retrieval-Augmented GenerationCode3
Systematic Knowledge Injection into Large Language Models via Diverse Augmentation for Domain-Specific RAGCode0
Cognify: Supercharging Gen-AI Workflows With Hierarchical AutotuningCode3
From PowerPoint UI Sketches to Web-Based Applications: Pattern-Driven Code Generation for GIS Dashboard Development Using Knowledge-Augmented LLMs, Context-Aware Visual Prompting, and the React Framework—0
AI-VERDE: A Gateway for Egalitarian Access to Large Language Model-Based Resources For Educational Institutions—0
Training Sparse Mixture Of Experts Text Embedding ModelsCode4
Elevating Legal LLM Responses: Harnessing Trainable Logical Structures and Semantic Knowledge with Legal ReasoningCode0
Graph RAG-Tool FusionCode1
Optimizing Knowledge Integration in Retrieval-Augmented Generation with Self-Selection—0
C-3PO: Compact Plug-and-Play Proxy Optimization to Achieve Human-like Retrieval-Augmented Generation—0
Towards Copyright Protection for Knowledge Bases of Retrieval-augmented Language Models via Reasoning—0
Combining Large Language Models with Static Analyzers for Code Review GenerationCode1
RALLRec: Improving Retrieval Augmented Large Language Model Recommendation with Representation LearningCode1
GraNNite: Enabling High-Performance Execution of Graph Neural Networks on Resource-Constrained Neural Processing UnitsCode0
AutoAgent: A Fully-Automated and Zero-Code Framework for LLM AgentsCode9
Enhancing Financial Time-Series Forecasting with Retrieval-Augmented Large Language Models—0
APE: Faster and Longer Context-Augmented Generation via Adaptive Parallel EncodingCode1
DeepThink: Aligning Language Models with Domain-Specific User Intents—0
Knowledge Graph-Guided Retrieval Augmented GenerationCode2
Towards Trustworthy Retrieval Augmented Generation for Large Language Models: A SurveyCode2
RAG-Verus: Repository-Level Program Verification with LLMs using Retrieval Augmented Generation—0
Agentic Reasoning: Reasoning LLMs with Tools for the Deep ResearchCode0
Efficient Knowledge Feeding to Language Models: A Novel Integrated Encoder-Decoder Architecture—0
Enhancing Health Information Retrieval with RAG by Prioritizing Topical Relevance and Factual Accuracy—0
LLMs to Support a Domain Specific Knowledge Assistant—0
MRAMG-Bench: A Comprehensive Benchmark for Advancing Multimodal Retrieval-Augmented Multimodal GenerationCode1
MedRAG: Enhancing Retrieval-augmented Generation with Knowledge Graph-Elicited Reasoning for Healthcare CopilotCode3
Enhancing Online Learning Efficiency Through Heterogeneous Resource Integration with a Multi-Agent RAG System—0
Experiments with Large Language Models on Retrieval-Augmented Generation for Closed-Source Simulation Software—0
Cache-Craft: Managing Chunk-Caches for Efficient Retrieval-Augmented Generation—0
MARAGE: Transferable Multi-Model Adversarial Attack for Retrieval-Augmented Generation Data Extraction—0
Spatial-RAG: Spatial Retrieval Augmented Generation for Real-World Geospatial Reasoning Questions—0
SCALM: Detecting Bad Practices in Smart Contracts Through LLMs—0
Open Foundation Models in Healthcare: Challenges, Paradoxes, and Opportunities with GenAI Driven Personalized Prescription—0
Conversation AI Dialog for Medicare powered by Finetuning and Retrieval Augmented Generation—0
Rankify: A Comprehensive Python Toolkit for Retrieval, Re-Ranking, and Retrieval-Augmented GenerationCode4
Personalization Toolkit: Training Free Personalization of Large Vision Language Models—0
OverThink: Slowdown Attacks on Reasoning LLMsCode1
LLMSecConfig: An LLM-Based Approach for Fixing Software Container Misconfigurations—0
Knowledge Synthesis of Photosynthesis Research Using a Large Language Model—0
VideoRAG: Retrieval-Augmented Generation with Extreme Long-Context VideosCode7
Show:102550
← PrevPage 16 of 43Next →

No leaderboard results yet.