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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 1801–1850 of 2111 papers

TitleStatusHype
Towards Unlocking Insights from Logbooks Using AI—0
Accelerating Inference of Retrieval-Augmented Generation via Sparse Context Selection—0
SynthAI: A Multi Agent Generative AI Framework for Automated Modular HLS Design GenerationCode1
Certifiably Robust RAG against Retrieval CorruptionCode1
Perception of Knowledge Boundary for Large Language Models through Semi-open-ended Question Answering—0
G3: An Effective and Adaptive Framework for Worldwide Geolocalization Using Large Multi-Modality ModelsCode1
RaFe: Ranking Feedback Improves Query Rewriting for RAG—0
HippoRAG: Neurobiologically Inspired Long-Term Memory for Large Language ModelsCode7
SearchLVLMs: A Plug-and-Play Framework for Augmenting Large Vision-Language Models by Searching Up-to-Date Internet Knowledge—0
TrojanRAG: Retrieval-Augmented Generation Can Be Backdoor Driver in Large Language ModelsCode0
Automated Evaluation of Retrieval-Augmented Language Models with Task-Specific Exam GenerationCode2
FlashRAG: A Modular Toolkit for Efficient Retrieval-Augmented Generation Research—0
FiDeLiS: Faithful Reasoning in Large Language Model for Knowledge Graph Question Answering—0
RAG-RLRC-LaySum at BioLaySumm: Integrating Retrieval-Augmented Generation and Readability Control for Layman Summarization of Biomedical TextsCode0
The 2nd FutureDial Challenge: Dialog Systems with Retrieval Augmented Generation (FutureDial-RAG)Code1
Generative AI in Cybersecurity: A Comprehensive Review of LLM Applications and Vulnerabilities—0
Can Github issues be solved with Tree Of Thoughts?Code0
Question-Based Retrieval using Atomic Units for Enterprise RAG—0
KG-RAG: Bridging the Gap Between Knowledge and Creativity—0
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
Control Token with Dense Passage Retrieval—0
From Questions to Insightful Answers: Building an Informed Chatbot for University Resources—0
Evaluation of Retrieval-Augmented Generation: A SurveyCode2
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
ERAGent: Enhancing Retrieval-Augmented Language Models with Improved Accuracy, Efficiency, and PersonalizationCode1
Self-Improving Customer Review Response Generation Based on LLMs—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
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
Overcoming LLM Challenges using RAG-Driven Precision in Coffee Leaf Disease Remediation—0
GAIA: A General AI Assistant for Intelligent Accelerator Operations—0
RAG-based Explainable Prediction of Road Users Behaviors for Automated Driving using Knowledge Graphs and Large Language Models—0
Integrating A.I. in Higher Education: Protocol for a Pilot Study with 'SAMCares: An Adaptive Learning Hub'Code0
Towards a Search Engine for Machines: Unified Ranking for Multiple Retrieval-Augmented Large Language ModelsCode0
GRAMMAR: Grounded and Modular Methodology for Assessment of Closed-Domain Retrieval-Augmented Language ModelCode0
RAG and RAU: A Survey on Retrieval-Augmented Language Model in Natural Language ProcessingCode3
ECC Analyzer: Extract Trading Signal from Earnings Conference Calls using Large Language Model for Stock Performance Prediction—0
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