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 1926–1950 of 2111 papers

TitleStatusHype
ERATTA: Extreme RAG for Table To Answers with Large Language Models—0
Robust Implementation of Retrieval-Augmented Generation on Edge-based Computing-in-Memory Architectures—0
Compressing Long Context for Enhancing RAG with AMR-based Concept Distillation—0
Characterizing the Dilemma of Performance and Index Size in Billion-Scale Vector Search and Breaking It with Second-Tier Memory—0
Self-Improving Customer Review Response Generation Based on LLMs—0
Leveraging Lecture Content for Improved Feedback: Explorations with GPT-4 and Retrieval Augmented Generation—0
Stochastic RAG: End-to-End Retrieval-Augmented Generation through Expected Utility Maximization—0
Comparative Analysis of Retrieval Systems in the Real World—0
Attribution in Scientific Literature: New Benchmark and Methods—0
GAIA: A General AI Assistant for Intelligent Accelerator Operations—0
Overcoming LLM Challenges using RAG-Driven Precision in Coffee Leaf Disease Remediation—0
Integrating A.I. in Higher Education: Protocol for a Pilot Study with 'SAMCares: An Adaptive Learning Hub'Code0
RAG-based Explainable Prediction of Road Users Behaviors for Automated Driving using Knowledge Graphs and Large Language Models—0
GRAMMAR: Grounded and Modular Methodology for Assessment of Closed-Domain Retrieval-Augmented Language ModelCode0
Towards a Search Engine for Machines: Unified Ranking for Multiple Retrieval-Augmented Large Language ModelsCode0
ECC Analyzer: Extract Trading Signal from Earnings Conference Calls using Large Language Model for Stock Performance Prediction—0
Tabular Embedding Model (TEM): Finetuning Embedding Models For Tabular RAG Applications—0
Generative AI for Low-Carbon Artificial Intelligence of Things with Large Language Models—0
Tool Calling: Enhancing Medication Consultation via Retrieval-Augmented Large Language Models—0
Human-Imperceptible Retrieval Poisoning Attacks in LLM-Powered Applications—0
Retrieval-Augmented Generation with Knowledge Graphs for Customer Service Question Answering—0
Prompt Leakage effect and defense strategies for multi-turn LLM interactions—0
IryoNLP at MEDIQA-CORR 2024: Tackling the Medical Error Detection & Correction Task On the Shoulders of Medical Agents—0
Retrieval-Augmented Audio Deepfake Detection—0
Typos that Broke the RAG's Back: Genetic Attack on RAG Pipeline by Simulating Documents in the Wild via Low-level PerturbationsCode0
Show:102550
← PrevPage 78 of 85Next →

No leaderboard results yet.