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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 776–800 of 2111 papers

TitleStatusHype
CoT-RAG: Integrating Chain of Thought and Retrieval-Augmented Generation to Enhance Reasoning in Large Language Models—0
Secure Multifaceted-RAG for Enterprise: Hybrid Knowledge Retrieval with Security Filtering—0
Fashion-RAG: Multimodal Fashion Image Editing via Retrieval-Augmented Generation—0
RAG Without the Lag: Interactive Debugging for Retrieval-Augmented Generation Pipelines—0
SCRAG: Social Computing-Based Retrieval Augmented Generation for Community Response Forecasting in Social Media Environments—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
InstructRAG: Leveraging Retrieval-Augmented Generation on Instruction Graphs for LLM-Based Task Planning—0
Estimating Optimal Context Length for Hybrid Retrieval-augmented Multi-document SummarizationCode0
FreshStack: Building Realistic Benchmarks for Evaluating Retrieval on Technical Documents—0
A Human-AI Comparative Analysis of Prompt Sensitivity in LLM-Based Relevance JudgmentCode0
ARCeR: an Agentic RAG for the Automated Definition of Cyber Ranges—0
On the Feasibility of Using MultiModal LLMs to Execute AR Social Engineering Attacks—0
Towards Conversational AI for Human-Machine Collaborative MLOps—0
A Visual RAG Pipeline for Few-Shot Fine-Grained Product Classification—0
LayoutCoT: Unleashing the Deep Reasoning Potential of Large Language Models for Layout Generation—0
Timing Analysis Agent: Autonomous Multi-Corner Multi-Mode (MCMM) Timing Debugging with Timing Debug Relation Graph—0
ARise: Towards Knowledge-Augmented Reasoning via Risk-Adaptive Search—0
Exploring the Role of Knowledge Graph-Based RAG in Japanese Medical Question Answering with Small-Scale LLMs—0
Towards Automated Safety Requirements Derivation Using Agent-based RAG—0
ReZero: Enhancing LLM search ability by trying one-more-time—0
Efficient Distributed Retrieval-Augmented Generation for Enhancing Language Model Performance—0
DataMosaic: Explainable and Verifiable Multi-Modal Data Analytics through Extract-Reason-Verify—0
VDocRAG: Retrieval-Augmented Generation over Visually-Rich Documents—0
GNN-ACLP: Graph Neural Networks based Analog Circuit Link Prediction—0
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