用语言特征精准调控对话难度，让AI更懂学习者水平
Controlling Language Difficulty in Dialogues with Linguistic Features
- 通过可读性、句法、词汇三类特征量化文本复杂度
- 新指标Dilaprix与专家判断高度相关，验证有效性
- 比提示工程更稳定灵活，适合语言学习场景
大语言模型（LLMs）在第二语言习得中表现出色，尤其在模拟互动对话方面。但如何使生成回复的语言难度匹配学习者水平仍是挑战。本文提出一种教育对话系统中的语言熟练度控制框架，利用可读性特征（如Flesch-Kincaid等级）、句法特征（如句法树深度）和词汇特征（如简单词比例）来量化与调节文本复杂度。实验表明，在语言学标注对话数据上训练的LLM能实现更精确的语言难度调控，优于基于提示的方法，在灵活性与稳定性上均表现更优。为此，我们提出Dilaprix这一新指标，综合上述特征，与专家对语言难度的判断具有强相关性。实证结果显示，该方法在保持高对话质量的同时，显著提升了语言难度的可控性。
原文摘要 · Abstract (English)
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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