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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 1601–1650 of 2111 papers

TitleStatusHype
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
VLR-Bench: Multilingual Benchmark Dataset for Vision-Language Retrieval Augmented Generation—0
Are LLMs Correctly Integrated into Software Systems?—0
VoxRAG: A Step Toward Transcription-Free RAG Systems in Spoken Question Answering—0
VR-RAG: Open-vocabulary Species Recognition with RAG-Assisted Large Multi-Modal Models—0
Vul-RAG: Enhancing LLM-based Vulnerability Detection via Knowledge-level RAG—0
Ward: Provable RAG Dataset Inference via LLM Watermarks—0
WASHtsApp -- A RAG-powered WhatsApp Chatbot for supporting rural African clean water access, sanitation and hygiene—0
LLMs & XAI for Water Sustainability: Seasonal Water Quality Prediction with LIME Explainable AI and a RAG-based Chatbot for Insights—0
WavRAG: Audio-Integrated Retrieval Augmented Generation for Spoken Dialogue Models—0
Weaver: Foundation Models for Creative Writing—0
WeKnow-RAG: An Adaptive Approach for Retrieval-Augmented Generation Integrating Web Search and Knowledge Graphs—0
What are Models Thinking about? Understanding Large Language Model Hallucinations "Psychology" through Model Inner State Analysis—0
What External Knowledge is Preferred by LLMs? Characterizing and Exploring Chain of Evidence in Imperfect Context—0
What LLMs Miss in Recommendations: Bridging the Gap with Retrieval-Augmented Collaborative Signals—0
When Machine Unlearning Meets Retrieval-Augmented Generation (RAG): Keep Secret or Forget Knowledge?—0
When Pigs Get Sick: Multi-Agent AI for Swine Disease Detection—0
Which Neurons Matter in IR? Applying Integrated Gradients-based Methods to Understand Cross-Encoders—0
Why Uncertainty Estimation Methods Fall Short in RAG: An Axiomatic Analysis—0
WikiContradict: A Benchmark for Evaluating LLMs on Real-World Knowledge Conflicts from Wikipedia—0
Winning Solution For Meta KDD Cup' 24—0
WixQA: A Multi-Dataset Benchmark for Enterprise Retrieval-Augmented Generation—0
Worse than Zero-shot? A Fact-Checking Dataset for Evaluating the Robustness of RAG Against Misleading Retrievals—0
Writing Style Matters: An Examination of Bias and Fairness in Information Retrieval Systems—0
Xinyu: An Efficient LLM-based System for Commentary Generation—0
XRAG: Cross-lingual Retrieval-Augmented Generation—0
NV-Retriever: Improving text embedding models with effective hard-negative mining—0
Observations on Building RAG Systems for Technical Documents—0
Octopus v2: On-device language model for super agent—0
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