[2604.04450] Conversational Control with Ontologies for Large Language Models: A Lightweight Framework for Constrained Generation

Computer Science > Computation and Language arXiv:2604.04450 (cs) [Submitted on 6 Apr 2026] Title:Conversational Control with Ontologies for Large Language Models: A Lightweight Framework for Constrained Generation Authors:Barbara Gendron, Gaël Guibon, Mathieu d'Aquin View a PDF of the paper titled Conversational Control with Ontologies for Large Language Models: A Lightweight Framework for Constrained Generation, by Barbara Gendron and 2 other authors View PDF HTML (experimental) 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, l...