Controlling Language Difficulty in Dialogues with Linguistic Features

2025-09-17

AI Summary

This study addresses the mismatch between linguistic difficulty and learner proficiency in second-language (L2) conversational systems. Methodologically, we propose a controllable difficulty modulation framework grounded in multidimensional linguistic features—namely, readability (e.g., Flesch-Kincaid Grade Level), syntactic complexity (e.g., dependency tree depth), and lexical simplicity (e.g., proportion of high-frequency words)—to model text complexity; we then fine-tune large language models (LLMs) for fine-grained control over linguistic proficiency levels. A key contribution is the introduction of Dilaprix, a novel automatic evaluation metric designed to replace conventional prompt-based engineering for difficulty assessment. Experimental results demonstrate statistically significant improvements in difficulty controllability (p < 0.01) without compromising dialogue quality. Moreover, Dilaprix exhibits strong correlation with human expert ratings (r = 0.89) and outperforms baseline prompt-based methods in both flexibility and stability.

Technology Category

• Natural Language Processing: Conversational AI/Dialog Systems
• Machine Learning: Large Multimodal Models (LMMs)
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

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.

Problem

• Adapting LLM language difficulty to learner proficiency
• Controlling language proficiency in educational dialogue systems
• Quantifying and regulating text complexity using linguistic features

Innovation

• Leveraging linguistic features for difficulty control
• Training LLMs on annotated dialogue data
• Introducing Dilaprix metric for difficulty evaluation