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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 801–825 of 2111 papers

TitleStatusHype
Hallucination Detection in LLMs via Topological Divergence on Attention Graphs—0
XY-Cut++: Advanced Layout Ordering via Hierarchical Mask Mechanism on a Novel BenchmarkCode0
Understanding and Optimizing Multi-Stage AI Inference Pipelines—0
DioR: Adaptive Cognitive Detection and Contextual Retrieval Optimization for Dynamic Retrieval-Augmented Generation—0
GNN-ACLP: Graph Neural Networks based Analog Circuit Link Prediction—0
AutoStyle-TTS: Retrieval-Augmented Generation based Automatic Style Matching Text-to-Speech Synthesis—0
MMKB-RAG: A Multi-Modal Knowledge-Based Retrieval-Augmented Generation Framework—0
SlowFastVAD: Video Anomaly Detection via Integrating Simple Detector and RAG-Enhanced Vision-Language Model—0
ControlNET: A Firewall for RAG-based LLM System—0
HD-RAG: Retrieval-Augmented Generation for Hybrid Documents Containing Text and Hierarchical Tables—0
Semantic Commit: Helping Users Update Intent Specifications for AI Memory at Scale—0
HeteRAG: A Heterogeneous Retrieval-augmented Generation Framework with Decoupled Knowledge Representations—0
The Other Side of the Coin: Exploring Fairness in Retrieval-Augmented GenerationCode0
TP-RAG: Benchmarking Retrieval-Augmented Large Language Model Agents for Spatiotemporal-Aware Travel Planning—0
Out of Style: RAG's Fragility to Linguistic VariationCode0
Knowledge Graph-extended Retrieval Augmented Generation for Question Answering—0
RTLRepoCoder: Repository-Level RTL Code Completion through the Combination of Fine-Tuning and Retrieval Augmentation—0
Adopting Large Language Models to Automated System Integration—0
DRAFT-ing Architectural Design Decisions using LLMsCode0
RAG-VR: Leveraging Retrieval-Augmented Generation for 3D Question Answering in VR EnvironmentsCode0
PCA-RAG: Principal Component Analysis for Efficient Retrieval-Augmented Generation—0
AI-University: An LLM-based platform for instructional alignment to scientific classroomsCode0
A System for Comprehensive Assessment of RAG FrameworksCode0
ConceptFormer: Towards Efficient Use of Knowledge-Graph Embeddings in Large Language Models—0
CollEX -- A Multimodal Agentic RAG System Enabling Interactive Exploration of Scientific Collections—0
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