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Retrieval-augmented Generation

Papers

Showing 5175 of 2196 papers

TitleStatusHype
Medical Graph RAG: Towards Safe Medical Large Language Model via Graph Retrieval-Augmented GenerationCode4
SimpleDeepSearcher: Deep Information Seeking via Web-Powered Reasoning Trajectory SynthesisCode4
Retrieval-Augmented Generation for Knowledge-Intensive NLP TasksCode4
Retrieval-Augmented Generation for Large Language Models: A SurveyCode4
Benchmarking Retrieval-Augmented Generation for MedicineCode4
Improving Retrieval-Augmented Generation in Medicine with Iterative Follow-up QuestionsCode4
Rankify: A Comprehensive Python Toolkit for Retrieval, Re-Ranking, and Retrieval-Augmented GenerationCode4
Retrieval-Augmented Generation with Hierarchical KnowledgeCode4
R1-Searcher++: Incentivizing the Dynamic Knowledge Acquisition of LLMs via Reinforcement LearningCode4
Symbolic Prompt Program Search: A Structure-Aware Approach to Efficient Compile-Time Prompt OptimizationCode4
COS-Mix: Cosine Similarity and Distance Fusion for Improved Information RetrievalCode4
Generative Representational Instruction TuningCode4
s3: You Don't Need That Much Data to Train a Search Agent via RLCode4
OpenResearcher: Unleashing AI for Accelerated Scientific ResearchCode3
FlexRAG: A Flexible and Comprehensive Framework for Retrieval-Augmented GenerationCode3
Panza: Design and Analysis of a Fully-Local Personalized Text Writing AssistantCode3
From human experts to machines: An LLM supported approach to ontology and knowledge graph constructionCode3
Parametric Retrieval Augmented GenerationCode3
A Smart Multimodal Healthcare Copilot with Powerful LLM ReasoningCode3
Fact, Fetch, and Reason: A Unified Evaluation of Retrieval-Augmented GenerationCode3
Ask in Any Modality: A Comprehensive Survey on Multimodal Retrieval-Augmented GenerationCode3
RAGEval: Scenario Specific RAG Evaluation Dataset Generation FrameworkCode3
OmniThink: Expanding Knowledge Boundaries in Machine Writing through ThinkingCode3
PathRAG: Pruning Graph-based Retrieval Augmented Generation with Relational PathsCode3
MoC: Mixtures of Text Chunking Learners for Retrieval-Augmented Generation SystemCode3
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