DIY-MKG：一个基于大语言模型的多语言学习系统
DIY-MKG: An LLM-Based Polyglot Language Learning System
AI总结：
DIY-MKG是一个基于大语言模型的开源多语言学习系统，通过构建个性化词汇知识图谱、自适应测验生成和用户反馈机制，解决了现有多语言学习工具在跨语言联系、个性化和认知卸载方面的不足。
摘要：
现有的语言学习工具，即使是那些由大语言模型（LLMs）驱动的工具，往往缺乏对多语言学习者在多种语言词汇之间建立语言联系的支持，对个人学习进度或需求的定制能力有限，并且存在有害的认知卸载问题。为了解决这些局限性，我们设计了“自己动手构建多语言知识图谱”（DIY-MKG），这是一个支持多语言学习的开源系统。DIY-MKG允许用户构建个性化的词汇知识图谱，这些图谱通过由大语言模型建议的相关词进行选择性扩展而构建。该系统还通过丰富的注释功能和自适应复习模块进一步增强学习效果，该模块利用大语言模型进行动态、个性化的测验生成。此外，DIY-MKG允许用户标记不正确的测验问题，同时提高用户参与度，并为提示词优化提供反馈回路。我们对DIY-MKG中基于大语言模型组件的评估表明，词汇扩展在多种语言中是可靠且公平的，并且生成的测验高度准确，验证了DIY-MKG的鲁棒性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
