Ontology-based constraints combined with hybrid fine-tuning enable consistent control over LLM conversational outputs on proficiency and polarity tasks, outperforming baselines across seven models.
InProceedings of the 36th International Conference on Machine Learning, volume 97 ofProceedings of Machine Learning Research, pages 2790–2799
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Conversational Control with Ontologies for Large Language Models: A Lightweight Framework for Constrained Generation
Ontology-based constraints combined with hybrid fine-tuning enable consistent control over LLM conversational outputs on proficiency and polarity tasks, outperforming baselines across seven models.