Summary of Llms4life: Large Language Models For Ontology Learning in Life Sciences, by Nadeen Fathallah et al.
LLMs4Life: Large Language Models for Ontology Learning in Life Sciences
by Nadeen Fathallah, Steffen Staab, Alsayed Algergawy
First submitted to arxiv on: 2 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper explores the challenges faced by Large Language Models (LLMs) when learning ontologies in complex domains like life sciences. The existing models struggle to generate hierarchically structured ontologies with rich connections and comprehensive class coverage due to limitations on token generation and inadequate domain adaptation. To address these issues, the authors extend the NeOn-GPT pipeline using advanced prompt engineering techniques and ontology reuse to enhance the generated ontologies’ domain-specific reasoning and structural depth. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using special computers called Large Language Models (LLMs) to learn complex systems of concepts called ontologies. Ontologies are like dictionaries, but instead of words, they define what things mean in a specific field, like biology or medicine. The problem is that these LLMs struggle to create really good ontologies because they can only say so many things and don’t understand the special language of each field very well. To help them do better, the authors came up with new ways to use the LLMs and old ontologies together. They tested this on a big project called AquaDiva that studies water ecosystems. The results show that these LLMs can actually be pretty good at creating useful ontologies. |
Keywords
» Artificial intelligence » Domain adaptation » Gpt » Prompt » Token
