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.
Abstract (translated)
基于大语言模型（LLMs）的对话代理近期已成为人机交互的强大工具。然而，其黑盒特性导致可预测性不足且缺乏个性化，而可控生成可解决这些问题。本研究提出一种端到端方法，通过对话相关方面的本体定义，实现对LLM输出的模块化与可解释控制。关键方面被建模为约束条件，并进一步对LLM进行微调以生成相应内容。为验证方法有效性，我们探索了两个针对核心对话方面的任务：英语 proficiency 等级与内容极性特征。通过对七个最先进的开源对话LLM采用混合微调流程，结果表明该方法持续优于预训练基线模型，甚至在较小模型上同样有效。除定量提升外，该框架保持模型无关性、轻量级与可解释性，支持可复用的控制策略并扩展至新领域与交互目标。此方法增强了与策略指令的对齐性，并证明了本体驱动控制在对话系统中的有效性。
URL
https://arxiv.org/abs/2604.04450
