• While LLMs can be trained for various domains, they may still require domain-specific data to overcome generic generalization strategies.
  • Effective solutions with LLMs rely on understanding ontology and entity definitions, enabling the capture of specific entities and properties.
  • Data annotation and enrichment are crucial steps in making LLMs more generic, but these processes require significant effort and resources.
  • The importance of domain knowledge is highlighted when training LLMs with a specific objective, necessitating the narrowing down of knowledge graphs for accurate answers.