m monad-knowledge
community · 4 papers

Knowledge Graph Extraction with LLMs

Retrieval-Augmented Generation (RAG) and knowledge graph construction using large language models form the core focus, addressing how structured and unstructured information can be extracted, organized, and retrieved to enhance LLM outputs. Graph-based retrieval methods are explored alongside techniques for extracting structured information from scientific text, with LLMs serving as both the extraction engine and the reasoning backbone. A recurring sub-theme is improving the accuracy and utility of knowledge graphs through refined extraction pipelines integrated with RAG frameworks.

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Papers in this community

  • Structured information extraction from scientific text with large language models 2024 DOI ↗
  • The construction and refined extraction techniques of knowledge graph based on large language models 2026 DOI ↗
  • Retrieval-Augmented Generation (RAG) 2025 DOI ↗
  • Graph Retrieval-Augmented Generation: A Survey 2026 DOI ↗
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Methods, problems, datasets, and hardware

Fine-Tuning×4 Few-Shot Learning×1 Reinforcement Learning×1 Domain Adaptation×1 Long Short-Term Memory×1 Self-Supervised Learning×1 Sensor Fusion×1 Transfer Learning×1
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Neighbouring communities

WiFi CSI Multi-user Activity Sensing×2 Retrieval Augmented Language Models×1