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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 1726–1750 of 2111 papers

TitleStatusHype
Practical Poisoning Attacks against Retrieval-Augmented Generation—0
P-RAG: Progressive Retrieval Augmented Generation For Planning on Embodied Everyday Task—0
PRAGyan -- Connecting the Dots in Tweets—0
PR-Attack: Coordinated Prompt-RAG Attacks on Retrieval-Augmented Generation in Large Language Models via Bilevel Optimization—0
Privacy-Aware RAG: Secure and Isolated Knowledge Retrieval—0
Privacy-Preserving Customer Support: A Framework for Secure and Scalable Interactions—0
Privacy-Preserving Retrieval-Augmented Generation with Differential Privacy—0
Probing Causality Manipulation of Large Language Models—0
Probing-RAG: Self-Probing to Guide Language Models in Selective Document Retrieval—0
Project Riley: Multimodal Multi-Agent LLM Collaboration with Emotional Reasoning and Voting—0
Prompt Generate Train (PGT): Few-shot Domain Adaption of Retrieval Augmented Generation Models for Open Book Question-Answering—0
Prompt Perturbation in Retrieval-Augmented Generation based Large Language Models—0
Prompt-RAG: Pioneering Vector Embedding-Free Retrieval-Augmented Generation in Niche Domains, Exemplified by Korean Medicine—0
Provenance: A Light-weight Fact-checker for Retrieval Augmented LLM Generation Output—0
Provence: efficient and robust context pruning for retrieval-augmented generation—0
Public Discourse Sandbox: Facilitating Human and AI Digital Communication Research—0
QE-RAG: A Robust Retrieval-Augmented Generation Benchmark for Query Entry Errors—0
QHackBench: Benchmarking Large Language Models for Quantum Code Generation Using PennyLane Hackathon Challenges—0
QualBench: Benchmarking Chinese LLMs with Localized Professional Qualifications for Vertical Domain Evaluation—0
Quantifying reliance on external information over parametric knowledge during Retrieval Augmented Generation (RAG) using mechanistic analysis—0
QUB-Cirdan at "Discharge Me!": Zero shot discharge letter generation by open-source LLM—0
QueEn: A Large Language Model for Quechua-English Translation—0
Query Optimization for Parametric Knowledge Refinement in Retrieval-Augmented Large Language Models—0
Query Performance Explanation through Large Language Model for HTAP Systems—0
Query pipeline optimization for cancer patient question answering systems—0
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