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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 826–850 of 2111 papers

TitleStatusHype
Parametric Retrieval Augmented GenerationCode3
PISCO: Pretty Simple Compression for Retrieval-Augmented Generation—0
Provence: efficient and robust context pruning for retrieval-augmented generation—0
SEAL: Speech Embedding Alignment Learning for Speech Large Language Model with Retrieval-Augmented Generation—0
An AI-Driven Live Systematic Reviews in the Brain-Heart Interconnectome: Minimizing Research Waste and Advancing Evidence SynthesisCode0
ASRank: Zero-Shot Re-Ranking with Answer Scent for Document Retrieval—0
Federated Retrieval Augmented Generation for Multi-Product Question Answering—0
Improving Retrieval-Augmented Generation through Multi-Agent Reinforcement LearningCode2
CFT-RAG: An Entity Tree Based Retrieval Augmented Generation Algorithm With Cuckoo FilterCode1
CG-RAG: Research Question Answering by Citation Graph Retrieval-Augmented LLMs—0
Advanced Real-Time Fraud Detection Using RAG-Based LLMs—0
Enhancing Intent Understanding for Ambiguous prompt: A Human-Machine Co-Adaption Strategy—0
GraPPI: A Retrieve-Divide-Solve GraphRAG Framework for Large-scale Protein-protein Interaction ExplorationCode0
Causal Graphs Meet Thoughts: Enhancing Complex Reasoning in Graph-Augmented LLMsCode0
Fast Think-on-Graph: Wider, Deeper and Faster Reasoning of Large Language Model on Knowledge GraphCode2
Chain-of-Retrieval Augmented Generation—0
StreamingRAG: Real-time Contextual Retrieval and Generation Framework—0
GraphRAG under Fire—0
CAPRAG: A Large Language Model Solution for Customer Service and Automatic Reporting using Vector and Graph Retrieval-Augmented Generation—0
RPO: Retrieval Preference Optimization for Robust Retrieval-Augmented Generation—0
Retrievals Can Be Detrimental: A Contrastive Backdoor Attack Paradigm on Retrieval-Augmented Diffusion Models—0
A RAG-Based Institutional Assistant—0
K-COMP: Retrieval-Augmented Medical Domain Question Answering With Knowledge-Injected CompressorCode0
Data Science Students Perspectives on Learning Analytics: An Application of Human-Led and LLM Content Analysis—0
Generating Diverse Q&A Benchmarks for RAG Evaluation with DataMorgana—0
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