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 801–825 of 2111 papers

TitleStatusHype
Fine-Tuning or Fine-Failing? Debunking Performance Myths in Large Language Models—0
Fine-Tuning or Retrieval? Comparing Knowledge Injection in LLMs—0
ER-RAG: Enhance RAG with ER-Based Unified Modeling of Heterogeneous Data Sources—0
FinRAGBench-V: A Benchmark for Multimodal RAG with Visual Citation in the Financial Domain—0
FinSage: A Multi-aspect RAG System for Financial Filings Question Answering—0
FinTextQA: A Dataset for Long-form Financial Question Answering—0
FinTMMBench: Benchmarking Temporal-Aware Multi-Modal RAG in Finance—0
First Token Probability Guided RAG for Telecom Question Answering—0
ERATTA: Extreme RAG for Table To Answers with Large Language Models—0
FiSTECH: Financial Style Transfer to Enhance Creativity without Hallucinations in LLMs—0
FIT-RAG: Black-Box RAG with Factual Information and Token Reduction—0
FLASH: Federated Learning-Based LLMs for Advanced Query Processing in Social Networks through RAG—0
FlashRAG: A Modular Toolkit for Efficient Retrieval-Augmented Generation Research—0
CAISSON: Concept-Augmented Inference Suite of Self-Organizing Neural Networks—0
CAFE: Retrieval Head-based Coarse-to-Fine Information Seeking to Enhance Multi-Document QA Capability—0
Flippi: End To End GenAI Assistant for E-Commerce—0
ENWAR: A RAG-empowered Multi-Modal LLM Framework for Wireless Environment Perception—0
EnronQA: Towards Personalized RAG over Private Documents—0
FoRAG: Factuality-optimized Retrieval Augmented Generation for Web-enhanced Long-form Question Answering—0
Formal Language Knowledge Corpus for Retrieval Augmented Generation—0
CLI-RAG: A Retrieval-Augmented Framework for Clinically Structured and Context Aware Text Generation with LLMs—0
Fortify Your Foundations: Practical Privacy and Security for Foundation Model Deployments In The Cloud—0
Found in the Middle: Calibrating Positional Attention Bias Improves Long Context Utilization—0
FRAG: A Flexible Modular Framework for Retrieval-Augmented Generation based on Knowledge Graphs—0
Cache-Craft: Managing Chunk-Caches for Efficient Retrieval-Augmented Generation—0
Show:102550
← PrevPage 33 of 85Next →

No leaderboard results yet.