@inproceedings{gendron-etal-2026-conversational,
    title = "Conversational Control with Ontologies for Large Language Models: A Lightweight Framework for Constrained Generation",
    author = "Gendron, Barbara  and
      Guibon, Gael  and
      d{'}Aquin, Mathieu",
    editor = "S{\'e}rasset, Gilles  and
      Gkirtzou, Katerina  and
      Cochez, Michael  and
      Kalo, Jan-Christoph",
    booktitle = "Proceedings of the Knowledge Graphs and Large Language Models Workshop ({KG}-{LLM}) @ {LREC}26",
    month = may,
    year = "2026",
    address = "Palma, Mallorca (Spain)",
    publisher = "ELRA Language Resources Association (ELRA)",
    url = "https://preview.aclanthology.org/paragraph-normalization/2026.kallm-1.3/",
    doi = "10.63317/446x4ysrhfeq",
    pages = "20--35",
    abstract = "Conversational agents based on Large Language Models (LLMs) have recently emerged as powerful tools for human-computer interaction. Nevertheless, their black-box nature implies challenges in predictability and a lack of personalization, both of which can be addressed by controlled generation. This work proposes an end-to-end method to obtain modular and explainable control over LLM outputs through ontological definitions of aspects related to the conversation. Key aspects are modeled and used as constraints; we then further fine-tune the LLM to generate content accordingly. To validate our approach, we explore two tasks that tackle two key conversational aspects: the English proficiency level and the polarity profile of the content. Using a hybrid fine-tuning procedure on seven state-of-the-art, open-weight conversational LLMs, we show that our method consistently outperforms pre-trained baselines, even on smaller models. Beyond quantitative gains, the framework remains model-agnostic, lightweight and interpretable, enabling reusable control strategies that can be extended to new domains and interaction goals. This approach enhances alignment with strategy instructions and demonstrates the effectiveness of ontology-driven control in conversational systems."
}