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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

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Showing 1901–1950 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
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
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