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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 1801–1825 of 2111 papers

TitleStatusHype
Enhancing Scientific Reproducibility Through Automated BioCompute Object Creation Using Retrieval-Augmented Generation from Publications—0
Enhancing Software-Related Information Extraction via Single-Choice Question Answering with Large Language Models—0
Enhancing Talent Employment Insights Through Feature Extraction with LLM Finetuning—0
Enhancing Thyroid Cytology Diagnosis with RAG-Optimized LLMs and Pa-thology Foundation Models—0
Enhancing Tourism Recommender Systems for Sustainable City Trips Using Retrieval-Augmented Generation—0
Enhancing tutoring systems by leveraging tailored promptings and domain knowledge with Large Language Models—0
EnronQA: Towards Personalized RAG over Private Documents—0
ENWAR: A RAG-empowered Multi-Modal LLM Framework for Wireless Environment Perception—0
ERATTA: Extreme RAG for Table To Answers with Large Language Models—0
ER-RAG: Enhance RAG with ER-Based Unified Modeling of Heterogeneous Data Sources—0
ESGReveal: An LLM-based approach for extracting structured data from ESG reports—0
Establishing Performance Baselines in Fine-Tuning, Retrieval-Augmented Generation and Soft-Prompting for Non-Specialist LLM Users—0
Evaluating and Enhancing Large Language Models Performance in Domain-specific Medicine: Osteoarthritis Management with DocOA—0
Bias Evaluation and Mitigation in Retrieval-Augmented Medical Question-Answering Systems—0
Evaluating Consistencies in LLM responses through a Semantic Clustering of Question Answering—0
Evaluating Knowledge Graph Based Retrieval Augmented Generation Methods under Knowledge Incompleteness—0
Evaluating Memory in LLM Agents via Incremental Multi-Turn Interactions—0
Evaluating Quality of Answers for Retrieval-Augmented Generation: A Strong LLM Is All You Need—0
Evaluating Retrieval Augmented Generative Models for Document Queries in Transportation Safety—0
Evaluating Self-Generated Documents for Enhancing Retrieval-Augmented Generation with Large Language Models—0
Evaluating Students' Open-ended Written Responses with LLMs: Using the RAG Framework for GPT-3.5, GPT-4, Claude-3, and Mistral-Large—0
Evaluating the Effect of Retrieval Augmentation on Social Biases—0
Evaluating the Impact of Advanced LLM Techniques on AI-Lecture Tutors for a Robotics Course—0
Evaluating the Performance of RAG Methods for Conversational AI in the Airport Domain—0
Evaluating the Retrieval Component in LLM-Based Question Answering Systems—0
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