Large Language Model based Situational Dialogues for Second Language Learning
This provides a scalable solution for language learners to practice scenario-based conversations, though it is incremental in applying LLMs to a specific educational domain.
The paper tackles the lack of conversational practice opportunities in second language learning by proposing situational dialogue models fine-tuned on large language models, which effectively perform on both training and unseen topics, and introduces a novel automatic evaluation method using fine-tuned LLMs to address expensive human evaluations.
In second language learning, scenario-based conversation practice is important for language learners to achieve fluency in speaking, but students often lack sufficient opportunities to practice their conversational skills with qualified instructors or native speakers. To bridge this gap, we propose situational dialogue models for students to engage in conversational practice. Our situational dialogue models are fine-tuned on large language models (LLMs), with the aim of combining the engaging nature of an open-ended conversation with the focused practice of scenario-based tasks. Leveraging the generalization capabilities of LLMs, we demonstrate that our situational dialogue models perform effectively not only on training topics but also on topics not encountered during training. This offers a promising solution to support a wide range of conversational topics without extensive manual work. Additionally, research in the field of dialogue systems still lacks reliable automatic evaluation metrics, leading to human evaluation as the gold standard (Smith et al., 2022), which is typically expensive. To address the limitations of existing evaluation methods, we present a novel automatic evaluation method that employs fine-tuned LLMs to efficiently and effectively assess the performance of situational dialogue models.