@inproceedings{caldas-etal-2026-specializing,
    title = "Specializing a Small Language Model for Closed-Domain {P}ortuguese {RAG} using Knowledge Graph Supervision",
    author = "Caldas, Josu{\'e}  and
      Souza, Elvis de  and
      Silva, Patr{\'i}cia  and
      Pacheco, Marco",
    editor = "Souza, Marlo  and
      de-Dios-Flores, Iria  and
      Santos, Diana  and
      Freitas, Larissa  and
      Souza, Jackson Wilke da Cruz  and
      Ribeiro, Eug{\'e}nio",
    booktitle = "Proceedings of the 17th International Conference on Computational Processing of {P}ortuguese ({PROPOR} 2026) - Vol. 1",
    month = apr,
    year = "2026",
    address = "Salvador, Brazil",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-dnd/2026.propor-1.28/",
    pages = "281--290",
    ISBN = "979-8-89176-387-6",
    abstract = "Fine-tuned small language models (SLMs) have emerged as effective alternatives for closed-domain tasks, where large language models (LLMs) often lack sufficient parametric knowledge. This study presents a methodology for adapting a small language model to a closed-domain question answering (Q A) task. For each question, the model is trained to output an answer based on the most relevant context passage, among ten provided candidates, thus reproducing the logic of a Retrieval-Augmented Generation (RAG) framework. The fine-tuning data were derived from PetroKGraph, an existing knowledge graph built from Portuguese-language resources in the oil and gas (O G) domain. Experimental results show that the fine-tuned model achieves a 20 percentage points accuracy improvement over the base model on closed-domain questions. It also surpasses GPT-4o and GPT-4o Mini by 12 and 25 points, respectively. Moreover, its performance on general-domain tasks remains comparable to that of the base model, indicating that the specialized model effectively learned domain specific knowledge while maintaining general reasoning capabilities."
}