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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 1301–1325 of 2111 papers

TitleStatusHype
SearchLVLMs: A Plug-and-Play Framework for Augmenting Large Vision-Language Models by Searching Up-to-Date Internet Knowledge—0
Unanswerability Evaluation for Retrieval Augmented Generation—0
UncertaintyRAG: Span-Level Uncertainty Enhanced Long-Context Modeling for Retrieval-Augmented Generation—0
Understanding and Optimizing Multi-Stage AI Inference Pipelines—0
Understanding Inequality of LLM Fact-Checking over Geographic Regions with Agent and Retrieval models—0
Unimib Assistant: designing a student-friendly RAG-based chatbot for all their needs—0
UniMS-RAG: A Unified Multi-source Retrieval-Augmented Generation for Personalized Dialogue Systems—0
UniOQA: A Unified Framework for Knowledge Graph Question Answering with Large Language Models—0
Unlearning Climate Misinformation in Large Language Models—0
Unlocking Historical Clinical Trial Data with ALIGN: A Compositional Large Language Model System for Medical Coding—0
Unlocking Multi-View Insights in Knowledge-Dense Retrieval-Augmented Generation—0
Unveiling and Consulting Core Experts in Retrieval-Augmented MoE-based LLMs—0
Unveiling Knowledge Utilization Mechanisms in LLM-based Retrieval-Augmented Generation—0
A Theory for Token-Level Harmonization in Retrieval-Augmented Generation—0
URAG: Implementing a Unified Hybrid RAG for Precise Answers in University Admission Chatbots -- A Case Study at HCMUT—0
Using GPT Models for Qualitative and Quantitative News Analytics in the 2024 US Presidental Election Process—0
ValuesRAG: Enhancing Cultural Alignment Through Retrieval-Augmented Contextual Learning—0
VDocRAG: Retrieval-Augmented Generation over Visually-Rich Documents—0
Vendi-RAG: Adaptively Trading-Off Diversity And Quality Significantly Improves Retrieval Augmented Generation With LLMs—0
VERA: Validation and Enhancement for Retrieval Augmented systems—0
VERA: Validation and Evaluation of Retrieval-Augmented Systems—0
Vietnamese Legal Information Retrieval in Question-Answering System—0
VISA: Retrieval Augmented Generation with Visual Source Attribution—0
VisDoM: Multi-Document QA with Visually Rich Elements Using Multimodal Retrieval-Augmented Generation—0
Visual RAG: Expanding MLLM visual knowledge without fine-tuning—0
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