DIY-MKG: An LLM-Based Polyglot Language Learning System

TL;DR

DIY-MKG is an open-source, LLM-powered multilingual learning system that enables personalized vocabulary graph building, adaptive quizzes, and user feedback to improve polyglot language learning.

Contribution

It introduces a novel system that supports polyglot learners with personalized knowledge graphs, adaptive quizzes, and feedback mechanisms, enhancing language learning tools.

Findings

Vocabulary expansion is reliable across multiple languages.

Generated quizzes are highly accurate.

User engagement is increased through feedback features.

Abstract

Existing language learning tools, even those powered by Large Language Models (LLMs), often lack support for polyglot learners to build linguistic connections across vocabularies in multiple languages, provide limited customization for individual learning paces or needs, and suffer from detrimental cognitive offloading. To address these limitations, we design Do-It-Yourself Multilingual Knowledge Graph (DIY-MKG), an open-source system that supports polyglot language learning. DIY-MKG allows the user to build personalized vocabulary knowledge graphs, which are constructed by selective expansion with related words suggested by an LLM. The system further enhances learning through rich annotation capabilities and an adaptive review module that leverages LLMs for dynamic, personalized quiz generation. In addition, DIY-MKG allows users to flag incorrect quiz questions, simultaneously increasing user engagement and providing a feedback loop for prompt refinement. Our evaluation of LLM-based components in DIY-MKG shows that vocabulary expansion is reliable and fair across multiple languages, and that the generated quizzes are highly accurate, validating the robustness of DIY-MKG.