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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
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
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
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