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

2026-04-06

AI Summary

This work addresses the challenges of unpredictability and lack of personalization in large language models for dialogue generation, which hinder controllable output. The authors propose a lightweight, model-agnostic, and reusable ontology-driven control framework that defines dialogue attributes through an ontology to establish modular and interpretable constraints. By integrating a hybrid fine-tuning strategy, the framework effectively steers models to generate responses aligned with specified dimensions. Evaluated across seven open-source dialogue large language models, the approach consistently outperforms baseline methods on tasks involving English proficiency and content polarity, demonstrating strong performance even with smaller-scale models. Moreover, it significantly enhances controllability, cross-domain adaptability, and alignment with desired conversational strategies.

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.

Problem

• conversational control
• large language models
• constrained generation
• ontology
• personalization

Innovation

• ontological control
• constrained generation
• large language models
• conversational AI
• explainable AI