Large Language Model based Situational Dialogues for Second Language Learning

TL;DR

This paper introduces LLM-based situational dialogue models to enhance second language learning through scenario-based practice, demonstrating effective generalization and proposing a novel automatic evaluation method to replace costly human assessments.

Contribution

It presents fine-tuned large language models for situational dialogues in language learning and introduces a new automatic evaluation approach using LLMs.

Findings

Models perform well on unseen topics

Effective automatic evaluation method developed

Supports diverse conversational topics

Abstract

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.