用知识图谱约束大模型对话，让输出更可控可解释。
Conversational Control with Ontologies for Large Language Models: A Lightweight Framework for Constrained Generation
- 用本体定义对话关键属性作为约束条件，指导生成
- 在7个主流对话模型上提升输出一致性，小模型也有效
- 框架轻量可复用，适合需要精准控制的对话场景
基于大语言模型的对话系统虽强大，但其黑箱特性导致输出不可预测且缺乏个性化。本文提出一种端到端方法，通过对话相关属性的本体定义实现模块化、可解释的控制。关键属性被建模为约束，并对大模型进行微调以生成符合要求的内容。在英语水平和内容情感极性两个任务上，采用混合微调策略，在7个先进开源对话模型上均优于预训练基线，即使在小型模型上也表现稳定。该框架具有模型无关性、轻量化和可解释性，支持跨领域扩展，有效提升与策略指令的一致性，验证了本体驱动控制在对话系统中的有效性。
原文摘要 · Abstract (English)
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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