We present our work on predicting United Nations sustainable development goals (SDG) for university courses. We use an LLM named PaLM 2 to generate training data given a noisy human-authored course description input as input. We use this data to train several different smaller language models to predict SDGs for university courses. This work contributes to better university level adaptation of SDGs. The best performing model in our experiments was BART with an F1-score of 0.786.

翻译：我们展示了在预测大学课程联合国可持续发展目标（SDG）方面的研究。我们使用名为PaLM 2的大语言模型，基于嘈杂人工编写的课程描述输入生成训练数据。借助这些数据，我们训练了多个不同的较小语言模型，用于预测大学课程的可持续发展目标。这项研究有助于大学层面更好地适配可持续发展目标。实验中表现最佳的模型是BART，其F1分数达到0.786。