用大模型帮多语学习者自建词汇知识图谱，个性化复习更高效
DIY-MKG: An LLM-Based Polyglot Language Learning System
- 用户可自建多语言词汇图谱，由大模型推荐关联词扩展
- 生成精准互动测验，支持动态调整学习节奏
- 适合自学多语种、追求个性化学习的用户
现有语言学习工具，即使基于大语言模型（LLMs），仍难以支持多语学习者在多语言词汇间建立联系，定制化程度低，且易造成认知负担。为此，我们设计了开源系统 DIY-MKG，支持多语种语言学习。用户可基于大模型建议，自主构建个性化的词汇知识图谱，通过选择性扩展构建跨语言关联。系统还提供丰富标注功能与自适应复习模块，利用大模型动态生成个性化测验。用户可标记错误题目，提升参与度并形成提示优化反馈闭环。对 DIY-MKG 中大模型组件的评估表明，多语言词汇扩展可靠且公平，生成测验准确率高，验证了系统的鲁棒性。
原文摘要 · Abstract (English)
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.
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