Controlling Language Difficulty in Dialogues with Linguistic Features

• URL: http://arxiv.org/abs/2509.14545v1
• Date: Thu, 18 Sep 2025 02:22:43 GMT
• Title: Controlling Language Difficulty in Dialogues with Linguistic Features
• Authors: Shuyao Xu, Wenguang Wang, Handong Gao, Wei Kang, Long Qin, Weizhi Wang,
• Abstract summary: Training large language models (LLMs) on linguistically annotated dialogue data enables precise modulation of language proficiency.<n>Our approach achieves superior controllability of language proficiency while maintaining high dialogue quality.
• Score: 9.426180715334667
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) have emerged as powerful tools for supporting second language acquisition, particularly in simulating interactive dialogues for speaking practice. However, adapting the language difficulty of LLM-generated responses to match learners' proficiency levels remains a challenge. This work addresses this issue by proposing a framework for controlling language proficiency in educational dialogue systems. Our approach leverages three categories of linguistic features, readability features (e.g., Flesch-Kincaid Grade Level), syntactic features (e.g., syntactic tree depth), and lexical features (e.g., simple word ratio), to quantify and regulate text complexity. We demonstrate that training LLMs on linguistically annotated dialogue data enables precise modulation of language proficiency, outperforming prompt-based methods in both flexibility and stability. To evaluate this, we introduce Dilaprix, a novel metric integrating the aforementioned features, which shows strong correlation with expert judgments of language difficulty. Empirical results reveal that our approach achieves superior controllability of language proficiency while maintaining high dialogue quality.

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