title: DSTI at LLMs4OL 2024 Task A: Intrinsic versus extrinsic knowledge for
    type classification
summary: We introduce semantic towers, an extrinsic knowledge representation method,
  and compare it to intrinsic knowledge in large language models for ontology
  learning. Our experiments show a trade-off between performance and semantic
  grounding for extrinsic knowledge compared to a fine-tuned model intrinsic
  knowledge. We report our findings on the Large Language Models for Ontology
  Learning (LLMs4OL) 2024 challenge.
paper_authors._id: 66cd9a9cc6033c4079db7b52
paper_authors.name: Hanna Abi Akl
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-08-27T09:23:01.773Z
paper_authors._id: 60e9d2e4e844d12533b9fd22
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/60e9d2e4e844d12533b9fd22/qs9kjTEC-0QbEAOPShygi.jpeg
paper_authors.fullname: Hanna Abi Akl
paper_authors.type: user
paper_authors.user: HannaAbiAkl
paper_title: DSTI at LLMs4OL 2024 Task A: Intrinsic versus extrinsic knowledge for
    type classification
paper_summary: We introduce semantic towers, an extrinsic knowledge representation method,
  and compare it to intrinsic knowledge in large language models for ontology
  learning. Our experiments show a trade-off between performance and semantic
  grounding for extrinsic knowledge compared to a fine-tuned model intrinsic
  knowledge. We report our findings on the Large Language Models for Ontology
  Learning (LLMs4OL) 2024 challenge.
paper_ai_keywords: semantic towers extrinsic knowledge representation method intrinsic knowledge large language models ontology learning fine-tuned model semantic grounding LLMs4OL 2024 challenge