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 751–775 of 2111 papers

TitleStatusHype
CancerKG.ORG A Web-scale, Interactive, Verifiable Knowledge Graph-LLM Hybrid for Assisting with Optimal Cancer Treatment and Care—0
Evaluating Consistencies in LLM responses through a Semantic Clustering of Question Answering—0
Bias Evaluation and Mitigation in Retrieval-Augmented Medical Question-Answering Systems—0
AppAgent v2: Advanced Agent for Flexible Mobile Interactions—0
FABULA: Intelligence Report Generation Using Retrieval-Augmented Narrative Construction—0
Face4RAG: Factual Consistency Evaluation for Retrieval Augmented Generation in Chinese—0
Chain-of-Rank: Enhancing Large Language Models for Domain-Specific RAG in Edge Device—0
Facilitating Video Story Interaction with Multi-Agent Collaborative System—0
Fact-Aware Multimodal Retrieval Augmentation for Accurate Medical Radiology Report Generation—0
Fact-checking AI-generated news reports: Can LLMs catch their own lies?—0
Advancing Retrieval-Augmented Generation for Persian: Development of Language Models, Comprehensive Benchmarks, and Best Practices for Optimization—0
FACTOID: FACtual enTailment fOr hallucInation Detection—0
From Standalone LLMs to Integrated Intelligence: A Survey of Compound Al Systems—0
GEC-RAG: Improving Generative Error Correction via Retrieval-Augmented Generation for Automatic Speech Recognition Systems—0
GenDFIR: Advancing Cyber Incident Timeline Analysis Through Retrieval Augmented Generation and Large Language Models—0
Augmenting Large Language Models with Static Code Analysis for Automated Code Quality Improvements—0
Evaluating and Enhancing Large Language Models Performance in Domain-specific Medicine: Osteoarthritis Management with DocOA—0
CALLM: Understanding Cancer Survivors' Emotions and Intervention Opportunities via Mobile Diaries and Context-Aware Language Models—0
A Pilot Empirical Study on When and How to Use Knowledge Graphs as Retrieval Augmented Generation—0
From Questions to Insightful Answers: Building an Informed Chatbot for University Resources—0
FastRAG: Retrieval Augmented Generation for Semi-structured Data—0
Establishing Performance Baselines in Fine-Tuning, Retrieval-Augmented Generation and Soft-Prompting for Non-Specialist LLM Users—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
ESGReveal: An LLM-based approach for extracting structured data from ESG reports—0
Show:102550
← PrevPage 31 of 85Next →

No leaderboard results yet.