OntoTune is a novel self-training framework that leverages structured ontologies to organize domain knowledge in LLMs, similar to how humans use mind maps to connect concepts.

• Addresses fragmented understanding in domain-specific LLMs by using hierarchical conceptual knowledge
• Utilizes SNOMED CT medical ontology to structure medical domain knowledge
• Demonstrates improved performance on medical QA tasks compared to conventional fine-tuning
• Provides a more coherent and organized representation of domain-specific information

This research is particularly valuable for the medical domain, where accurate knowledge representation is critical for clinical decision support, medical education, and healthcare applications. OntoTune's approach could lead to more reliable and comprehensible AI systems for healthcare professionals.

OntoTune: Ontology-Driven Self-training for Aligning Large Language Models