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

ArXi:2604.04450v1 Announce Type: cross 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.