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 1901–1925 of 2111 papers

TitleStatusHype
Accelerating Inference of Retrieval-Augmented Generation via Sparse Context Selection—0
Towards Unlocking Insights from Logbooks Using AI—0
RaFe: Ranking Feedback Improves Query Rewriting for RAG—0
Perception of Knowledge Boundary for Large Language Models through Semi-open-ended Question Answering—0
SearchLVLMs: A Plug-and-Play Framework for Augmenting Large Vision-Language Models by Searching Up-to-Date Internet Knowledge—0
FlashRAG: A Modular Toolkit for Efficient Retrieval-Augmented Generation Research—0
FiDeLiS: Faithful Reasoning in Large Language Model for Knowledge Graph Question Answering—0
TrojanRAG: Retrieval-Augmented Generation Can Be Backdoor Driver in Large Language ModelsCode0
RAG-RLRC-LaySum at BioLaySumm: Integrating Retrieval-Augmented Generation and Readability Control for Layman Summarization of Biomedical TextsCode0
Generative AI in Cybersecurity: A Comprehensive Review of LLM Applications and Vulnerabilities—0
Question-Based Retrieval using Atomic Units for Enterprise RAG—0
KG-RAG: Bridging the Gap Between Knowledge and Creativity—0
Can Github issues be solved with Tree Of Thoughts?Code0
A Hybrid Framework with Large Language Models for Rare Disease Phenotyping—0
FinTextQA: A Dataset for Long-form Financial Question Answering—0
IM-RAG: Multi-Round Retrieval-Augmented Generation Through Learning Inner Monologues—0
Exploring the Potential of Large Language Models for Automation in Technical Customer Service—0
From Questions to Insightful Answers: Building an Informed Chatbot for University Resources—0
Control Token with Dense Passage Retrieval—0
DuetRAG: Collaborative Retrieval-Augmented Generation—0
A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models—0
Artificial Intelligence as the New Hacker: Developing Agents for Offensive Security—0
Automated Conversion of Static to Dynamic Scheduler via Natural Language—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
Remote Diffusion—0
Show:102550
← PrevPage 77 of 85Next →

No leaderboard results yet.