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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 1751–1775 of 2111 papers

TitleStatusHype
EdgeRAG: Online-Indexed RAG for Edge Devices—0
Efficient Context Selection for Long-Context QA: No Tuning, No Iteration, Just Adaptive-k—0
Efficient Distributed Retrieval-Augmented Generation for Enhancing Language Model Performance—0
EfficientEQA: An Efficient Approach for Open Vocabulary Embodied Question Answering—0
Efficient Federated Search for Retrieval-Augmented Generation—0
Efficient In-Domain Question Answering for Resource-Constrained Environments—0
Efficient Knowledge Feeding to Language Models: A Novel Integrated Encoder-Decoder Architecture—0
Efficient Learning Content Retrieval with Knowledge Injection—0
Efficient Title Reranker for Fast and Improved Knowledge-Intense NLP—0
Efficient VoIP Communications through LLM-based Real-Time Speech Reconstruction and Call Prioritization for Emergency Services—0
Eliciting Critical Reasoning in Retrieval-Augmented Language Models via Contrastive Explanations—0
Eliciting In-context Retrieval and Reasoning for Long-context Large Language Models—0
Embodied-RAG: General Non-parametric Embodied Memory for Retrieval and Generation—0
EMERGE: Integrating RAG for Improved Multimodal EHR Predictive Modeling—0
Empirical Guidelines for Deploying LLMs onto Resource-constrained Edge Devices—0
Empowering Agentic Video Analytics Systems with Video Language Models—0
End-to-End Bangla AI for Solving Math Olympiad Problem Benchmark: Leveraging Large Language Model Using Integrated Approach—0
End-to-End Table Question Answering via Retrieval-Augmented Generation—0
Engineering LLM Powered Multi-agent Framework for Autonomous CloudOps—0
Engineering RAG Systems for Real-World Applications: Design, Development, and Evaluation—0
Enhanced document retrieval with topic embeddings—0
Enhanced Multimodal RAG-LLM for Accurate Visual Question Answering—0
Enhanced Retrieval of Long Documents: Leveraging Fine-Grained Block Representations with Large Language Models—0
SeRTS: Self-Rewarding Tree Search for Biomedical Retrieval-Augmented Generation—0
Enhancing Cache-Augmented Generation (CAG) with Adaptive Contextual Compression for Scalable Knowledge Integration—0
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