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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

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Showing 401–425 of 2111 papers

TitleStatusHype
Hallucination Detection in LLMs via Topological Divergence on Attention Graphs—0
DataMosaic: Explainable and Verifiable Multi-Modal Data Analytics through Extract-Reason-Verify—0
SlowFastVAD: Video Anomaly Detection via Integrating Simple Detector and RAG-Enhanced Vision-Language Model—0
MMKB-RAG: A Multi-Modal Knowledge-Based Retrieval-Augmented Generation Framework—0
XY-Cut++: Advanced Layout Ordering via Hierarchical Mask Mechanism on a Novel BenchmarkCode0
AutoStyle-TTS: Retrieval-Augmented Generation based Automatic Style Matching Text-to-Speech Synthesis—0
A Survey of Personalization: From RAG to AgentCode2
GNN-ACLP: Graph Neural Networks based Analog Circuit Link Prediction—0
SymRTLO: Enhancing RTL Code Optimization with LLMs and Neuron-Inspired Symbolic Reasoning—0
Understanding and Optimizing Multi-Stage AI Inference Pipelines—0
DioR: Adaptive Cognitive Detection and Contextual Retrieval Optimization for Dynamic Retrieval-Augmented Generation—0
HD-RAG: Retrieval-Augmented Generation for Hybrid Documents Containing Text and Hierarchical Tables—0
HM-RAG: Hierarchical Multi-Agent Multimodal Retrieval Augmented GenerationCode2
ControlNET: A Firewall for RAG-based LLM System—0
HeteRAG: A Heterogeneous Retrieval-augmented Generation Framework with Decoupled Knowledge Representations—0
Semantic Commit: Helping Users Update Intent Specifications for AI Memory at Scale—0
Pneuma: Leveraging LLMs for Tabular Data Representation and Retrieval in an End-to-End SystemCode1
Knowledge Graph-extended Retrieval Augmented Generation for Question Answering—0
HyperCore: The Core Framework for Building Hyperbolic Foundation Models with Comprehensive ModulesCode1
The Other Side of the Coin: Exploring Fairness in Retrieval-Augmented GenerationCode0
RTLRepoCoder: Repository-Level RTL Code Completion through the Combination of Fine-Tuning and Retrieval Augmentation—0
PCA-RAG: Principal Component Analysis for Efficient Retrieval-Augmented Generation—0
Out of Style: RAG's Fragility to Linguistic VariationCode0
TP-RAG: Benchmarking Retrieval-Augmented Large Language Model Agents for Spatiotemporal-Aware Travel Planning—0
Adopting Large Language Models to Automated System Integration—0
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