DIY-MKG: An LLM-Based Polyglot Language Learning System
This addresses the problem of limited and non-adaptive tools for polyglot language learners, though it is incremental as it builds on existing LLM-based methods with new features.
The paper tackles the lack of polyglot support and customization in language learning tools by introducing DIY-MKG, an open-source system that builds personalized vocabulary knowledge graphs and generates adaptive quizzes using LLMs, with evaluations showing reliable vocabulary expansion and highly accurate quizzes across multiple languages.
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.