用本体论让语言模型的对话能力更可控且可解释
Towards Ontology-Based Descriptions of Conversations with Qualitatively-Defined Concepts
- 用语言描述构建量化概念，融入本体用于推理
- 在CEFR语言水平上实现一致且可解释的控制
- 适合需要精准对话控制的教育类AI应用
将大语言模型（LLMs）用作对话代理时，其可控性是一个关键挑战，尤其在确保响应可预测和用户个性化方面。本文提出一种基于本体的方法，对通常为定性描述的对话特征进行形式化定义。通过一组语言学描述符，我们推导出定性概念的定量定义，使其能被整合进本体以支持推理与一致性检查。该框架应用于对话中的语言能力水平控制任务，以CEFR语言熟练度等级为案例研究。这些定义以描述逻辑形式化，并纳入本体，指导大模型通过微调生成受控文本。实验结果表明，该方法提供了连贯且可解释的语言能力水平定义，提升了对话人工智能的透明度。
原文摘要 · Abstract (English)
The controllability of Large Language Models (LLMs) when used as conversational agents is a key challenge, particularly to ensure predictable and user-personalized responses. This work proposes an ontology-based approach to formally define conversational features that are typically qualitative in nature. By leveraging a set of linguistic descriptors, we derive quantitative definitions for qualitatively-defined concepts, enabling their integration into an ontology for reasoning and consistency checking. We apply this framework to the task of proficiency-level control in conversations, using CEFR language proficiency levels as a case study. These definitions are then formalized in description logic and incorporated into an ontology, which guides controlled text generation of an LLM through fine-tuning. Experimental results demonstrate that our approach provides consistent and explainable proficiency-level definitions, improving transparency in conversational AI.
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