大型语言模型在教育中的多语言性能偏差
Multilingual Performance Biases of Large Language Models in Education
AI总结：
本研究评估了大型语言模型在八种非英语语言及英语上的四项教育任务表现，发现性能与训练数据语言代表性相关，低资源语言表现较差，建议部署前验证目标语言性能。
摘要：
大型语言模型（LLMs）正日益被应用于教育场景中。这些应用已扩展到英语之外，尽管当前的LLMs仍主要以英语为中心。在本研究中，我们探究了它们在教育环境中用于非英语语言是否合理。我们评估了流行LLMs在四项教育任务上的表现：识别学生误解、提供针对性反馈、互动辅导以及翻译评分，涉及八种语言（普通话、印地语、阿拉伯语、德语、波斯语、泰卢固语、乌克兰语、捷克语）以及英语。我们发现，这些任务上的表现与训练数据中语言的代表量大致对应，资源较少的语言任务表现较差。尽管模型在大多数语言中表现尚可，但从英语到其他语言的频繁性能下降是显著的。因此，我们建议实践者在部署前首先验证LLM在其目标语言的教育任务上是否表现良好。
英文摘要：
Large language models (LLMs) are increasingly being adopted in educational settings. These applications expand beyond English, though current LLMs remain primarily English-centric. In this work, we ascertain if their use in education settings in non-English languages is warranted. We evaluated the performance of popular LLMs on four educational tasks: identifying student misconceptions, providing targeted feedback, interactive tutoring, and grading translations in eight languages (Mandarin, Hindi, Arabic, German, Farsi, Telugu, Ukrainian, Czech) in addition to English. We find that the performance on these tasks somewhat corresponds to the amount of language represented in training data, with lower-resource languages having poorer task performance. Although the models perform reasonably well in most languages, the frequent performance drop from English is significant. Thus, we recommend that practitioners first verify that the LLM works well in the target language for their educational task before deployment.
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。
