Controlling Language Difficulty in Dialogues with Linguistic Features

This paper proposes a framework for controlling language difficulty in LLM-generated dialogue responses for second language (L2) learners by using linguistic features (readability, syntactic, lexical) and introduces Dilaprix, a metric to quantify dialogue complexity. The work focuses on adapting AI dialogue systems to match learners' proficiency levels, particularly for speaking practice in language learning.

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, readabi

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