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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 1276–1300 of 2111 papers

TitleStatusHype
Athena: Retrieval-augmented Legal Judgment Prediction with Large Language Models—0
On the Capacity of Citation Generation by Large Language Models—0
Synthetic Interlocutors. Experiments with Generative AI to Prolong Ethnographic Encounters—0
Retrieval Augmented Spelling Correction for E-Commerce Applications—0
DynamicER: Resolving Emerging Mentions to Dynamic Entities for RAGCode0
Graph of Records: Boosting Retrieval Augmented Generation for Long-context Summarization with GraphsCode1
Parenting: Optimizing Knowledge Selection of Retrieval-Augmented Language Models with Parameter Decoupling and Tailored Tuning—0
FunnelRAG: A Coarse-to-Fine Progressive Retrieval Paradigm for RAG—0
Enhancing Retrieval-Augmented Audio Captioning with Generation-Assisted Multimodal Querying and Progressive Learning—0
EasyRAG: Efficient Retrieval-Augmented Generation Framework for Automated Network OperationsCode4
Model-based Large Language Model Customization as Service—0
STACKFEED: Structured Textual Actor-Critic Knowledge Base Editing with FeedBack—0
Beyond-RAG: Question Identification and Answer Generation in Real-Time Conversations—0
VisRAG: Vision-based Retrieval-augmented Generation on Multi-modality DocumentsCode4
Learning to Rank for Multiple Retrieval-Augmented Models through Iterative Utility Maximization—0
A Comparative Study of PDF Parsing Tools Across Diverse Document Categories—0
Honest AI: Fine-Tuning "Small" Language Models to Say "I Don't Know", and Reducing Hallucination in RAG—0
Retrieval Instead of Fine-tuning: A Retrieval-based Parameter Ensemble for Zero-shot Learning—0
Quebec Automobile Insurance Question-Answering With Retrieval-Augmented GenerationCode0
Synthetic Knowledge Ingestion: Towards Knowledge Refinement and Injection for Enhancing Large Language ModelsCode0
Toward General Instruction-Following Alignment for Retrieval-Augmented GenerationCode2
Enhancing Long Context Performance in LLMs Through Inner Loop Query Mechanism—0
Developing a Pragmatic Benchmark for Assessing Korean Legal Language Understanding in Large Language ModelsCode0
oRetrieval Augmented Generation for 10 Large Language Models and its Generalizability in Assessing Medical Fitness—0
A Methodology for Evaluating RAG Systems: A Case Study On Configuration Dependency ValidationCode0
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