@inproceedings{GarciaAlarcia2026LLMsSystems,
  author    = {Garc{\'\i}a Alarcia, Ram{\'o}n Mar{\'\i}a and Alhamadah, Ahmed and Elaasar, Maged and Golkar, Alessandro and Salado, Alejandro},
  title     = {Large Language Models for Systems and Ontological Modeling: Fine-tuning and Evaluation},
  booktitle = {23rd Annual Conference on Systems Engineering Research (CSER 2026)},
  month     = {apr},
  year      = {2026},
  url       = {https://mediatum.ub.tum.de/1851163},
  abstract  = {An increased number of specialized Large Language Models (LLMs) are being released recently, supporting a variety of domain-specific tasks at a fraction of the inference cost of general-purpose LLMs. The Systems Engineering community, with a fragmented landscape of modeling languages, could benefit from specialized LLMs for system modeling tasks, bringing down barriers that prevent adopting Model Based Systems Engineering (MBSE). However, the community does not yet have a well-established corpus of specialized LLMs, nor fine-tuning methodologies tailored to the specificities of its languages. In this paper, we contribute a methodology to fine-tune LLMs for small, underrepresented modeling languages. Using the generic Ontological Modeling Language (OML) as a reference, we explore the balance between the fine-tuning an LLM and using a general-purpose one.},
  keywords  = {ontology modeling, systems modeling, LLM, CoPilot, fine-tuning, OML},
  file      = {2026/2026_GarciaAlarcia_LLMsSystemsOntologicalModeling.pdf}
}