MatOnto Ontology (MATONTO)

subtask—selected for its manageable size—we explore the performance of open-source models under resource constraints. We fine-tune LLaMA 3.1–8B using LoRA adapters and evaluate various strategies including contrastive negative sampling, prompt inversion, and system prompt variation. Inspired by recent findings on prompt sensitivity, we adopt a cross-template setup where the model is trained with one prompt format and tested with another semantically equivalent variant. Our experiments suggest that prompt-decoupling can improve generalization and mitigate overfitting to specific phrasings. While our results are modest, they offer insights into the challenges of adapting LLMs to structured relation extraction tasks and highlight practical considerations for tuning under constrained resources.