DSTI at LLMs4OL 2024 Task A: Intrinsic versus extrinsic knowledge for
  type classification
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.
66cd9a9cc6033c4079db7b52
Hanna Abi Akl
claimed_verified
2024-08-27T09:23:01.773Z
60e9d2e4e844d12533b9fd22
https://cdn-avatars.huggingface.co/v1/production/uploads/60e9d2e4e844d12533b9fd22/qs9kjTEC-0QbEAOPShygi.jpeg
Hanna Abi Akl
user
HannaAbiAkl
DSTI at LLMs4OL 2024 Task A: Intrinsic versus extrinsic knowledge for
  type classification
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.
The study compares semantic towers, an extrinsic knowledge representation method, to intrinsic knowledge in large language models for ontology learning, highlighting performance trade-offs and semantic grounding.
semantic towers extrinsic knowledge intrinsic knowledge ontology learning LLMs4OL challenge