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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 1326–1350 of 2111 papers

TitleStatusHype
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
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