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Retrieval-augmented Generation

Papers

Showing 76100 of 2196 papers

TitleStatusHype
RAGLAB: A Modular and Research-Oriented Unified Framework for Retrieval-Augmented GenerationCode3
syftr: Pareto-Optimal Generative AICode3
From human experts to machines: An LLM supported approach to ontology and knowledge graph constructionCode3
GFM-RAG: Graph Foundation Model for Retrieval Augmented GenerationCode3
PoisonedRAG: Knowledge Corruption Attacks to Retrieval-Augmented Generation of Large Language ModelsCode3
PathRAG: Pruning Graph-based Retrieval Augmented Generation with Relational PathsCode3
GNN-RAG: Graph Neural Retrieval for Large Language Model ReasoningCode3
RAG and RAU: A Survey on Retrieval-Augmented Language Model in Natural Language ProcessingCode3
Beyond Quacking: Deep Integration of Language Models and RAG into DuckDBCode3
OpenResearcher: Unleashing AI for Accelerated Scientific ResearchCode3
BERGEN: A Benchmarking Library for Retrieval-Augmented GenerationCode3
MedTrinity-25M: A Large-scale Multimodal Dataset with Multigranular Annotations for MedicineCode3
OmniThink: Expanding Knowledge Boundaries in Machine Writing through ThinkingCode3
Fact, Fetch, and Reason: A Unified Evaluation of Retrieval-Augmented GenerationCode3
Panza: Design and Analysis of a Fully-Local Personalized Text Writing AssistantCode3
Benchmarking Multimodal Retrieval Augmented Generation with Dynamic VQA Dataset and Self-adaptive Planning AgentCode3
FlexRAG: A Flexible and Comprehensive Framework for Retrieval-Augmented GenerationCode3
MultiHop-RAG: Benchmarking Retrieval-Augmented Generation for Multi-Hop QueriesCode3
GRAG: Graph Retrieval-Augmented GenerationCode3
Multi-Head RAG: Solving Multi-Aspect Problems with LLMsCode3
Parametric Retrieval Augmented GenerationCode3
MMed-RAG: Versatile Multimodal RAG System for Medical Vision Language ModelsCode3
A Smart Multimodal Healthcare Copilot with Powerful LLM ReasoningCode3
Meta-Chunking: Learning Text Segmentation and Semantic Completion via Logical PerceptionCode3
Ask in Any Modality: A Comprehensive Survey on Multimodal Retrieval-Augmented GenerationCode3
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