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

• URL: http://arxiv.org/abs/2604.04450v1
• Date: Mon, 06 Apr 2026 05:54:21 GMT
• Title: Conversational Control with Ontologies for Large Language Models: A Lightweight Framework for Constrained Generation
• Abstract summary: This work proposes an end-to-end method to obtain modular and explainable control over LLM outputs.<n>Key aspects are modeled and used as constraints; we then further fine-tune the LLM to generate content accordingly.<n>We show that our method consistently outperforms pre-trained baselines, even on smaller models.
• Score: 2.748993665644782
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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