GUG 2nd seminar
Read more
25년 11월 5주차 그래프 오마카세
Why Ontologies are Key for Data Governance in the LLM Era? Reference Blog : https://medium.com/timbr-ai * 최근 LLM 도입을 검토하는 많은 기업 사이에서 온톨로지(Ontology)가 뜨거운 화두로 떠오르고 있습니다. 다들 잘 아시다시피, 온톨로지란 쉽게 말해 데이터 간의 관계와 의미를 정의하는 지도로 받아들일 수 있습니다. 단순히 데이터를 저장하는 것을
25년 11월 4주차 그래프 오마카세
NodeRAG: Structuring Graph-based RAG with Heterogeneous Nodes NodeRAG: Structuring Graph-based RAG with Heterogeneous NodesRetrieval-augmented generation (RAG) empowers large language models to access external and private corpus, enabling factually consistent responses in specific domains. By exploiting the inherent structure of the corpus, graph-based RAG methods further enrich this process by building
25년 11월 3주차 그래프 오마카세
When to use Graphs in RAG: A Comprehensive Analysis for Graph Retrieval-Augmented Generation When to use Graphs in RAG: A Comprehensive Analysis for Graph Retrieval-Augmented GenerationGraph retrieval-augmented generation (GraphRAG) has emerged as a powerful paradigm for enhancing large language models (LLMs) with external knowledge. It leverages graphs to model the
25년 11월 2주차 그래프 오마카세
GraphFrames: Architectural Evolution from GraphX for Big Data and AI Applications GraphFrames: an integrated API for mixing graph and relational queriesGraph data is prevalent in many domains, but it has usually required specialized engines to analyze. This design is onerous for users and precludes optimization across complete workflows. We…OpenReview.