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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
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
Enhancing classroom teaching with LLMs and RAG—0
Enhancing Cluster Resilience: LLM-agent Based Autonomous Intelligent Cluster Diagnosis System and Evaluation Framework—0
Enhancing Code Translation in Language Models with Few-Shot Learning via Retrieval-Augmented Generation—0
Enhancing Cross-Language Code Translation via Task-Specific Embedding Alignment in Retrieval-Augmented Generation—0
Enhancing E-commerce Product Title Translation with Retrieval-Augmented Generation and Large Language Models—0
Enhancing Financial Time-Series Forecasting with Retrieval-Augmented Large Language Models—0
Enhancing Health Information Retrieval with RAG by Prioritizing Topical Relevance and Factual Accuracy—0
Enhancing Intent Understanding for Ambiguous prompt: A Human-Machine Co-Adaption Strategy—0
Enhancing Large Language Model Performance To Answer Questions and Extract Information More Accurately—0
Enhancing Large Language Models (LLMs) for Telecommunications using Knowledge Graphs and Retrieval-Augmented Generation—0
Enhancing Large Language Models with Domain-specific Retrieval Augment Generation: A Case Study on Long-form Consumer Health Question Answering in Ophthalmology—0
Enhancing LLM Generation with Knowledge Hypergraph for Evidence-Based Medicine—0
Enhancing LLM Intelligence with ARM-RAG: Auxiliary Rationale Memory for Retrieval Augmented Generation—0
Enhancing LLMs for Power System Simulations: A Feedback-driven Multi-agent Framework—0
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