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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 1776–1800 of 2111 papers

TitleStatusHype
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
Enhancing Long Context Performance in LLMs Through Inner Loop Query Mechanism—0
Enhancing Multilingual Information Retrieval in Mixed Human Resources Environments: A RAG Model Implementation for Multicultural Enterprise—0
Enhancing Online Learning Efficiency Through Heterogeneous Resource Integration with a Multi-Agent RAG System—0
Enhancing Pancreatic Cancer Staging with Large Language Models: The Role of Retrieval-Augmented Generation—0
Enhancing Q&A Text Retrieval with Ranking Models: Benchmarking, fine-tuning and deploying Rerankers for RAG—0
Enhancing Q&A with Domain-Specific Fine-Tuning and Iterative Reasoning: A Comparative Study—0
Enhancing RAG with Active Learning on Conversation Records: Reject Incapables and Answer Capables—0
Enhancing Retrieval-Augmented LMs with a Two-stage Consistency Learning Compressor—0
Enhancing Retrieval for ESGLLM via ESG-CID -- A Disclosure Content Index Finetuning Dataset for Mapping GRI and ESRS—0
Enhancing Retrieval Performance: An Ensemble Approach For Hard Negative Mining—0
Enhancing Retrieval Processes for Language Generation with Augmented Queries—0
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