SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 771–780 of 2111 papers

TitleStatusHype
FastRAG: Retrieval Augmented Generation for Semi-structured Data—0
ESGReveal: An LLM-based approach for extracting structured data from ESG reports—0
Faux Polyglot: A Study on Information Disparity in Multilingual Large Language Models—0
FeB4RAG: Evaluating Federated Search in the Context of Retrieval Augmented Generation—0
Federated In-Context LLM Agent Learning—0
Federated Learning and RAG Integration: A Scalable Approach for Medical Large Language Models—0
Calibrated Decision-Making through LLM-Assisted Retrieval—0
Federated Retrieval-Augmented Generation: A Systematic Mapping Study—0
Federated Retrieval Augmented Generation for Multi-Product Question Answering—0
ER-RAG: Enhance RAG with ER-Based Unified Modeling of Heterogeneous Data Sources—0
Show:102550
← PrevPage 78 of 212Next →

No leaderboard results yet.