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：我们介绍了针对大学课程预测联合国可持续发展目标（SDG）的研究工作。我们使用名为PaLM 2的大语言模型，以人工撰写的含噪声课程描述作为输入生成训练数据，并利用这些数据训练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。本研究有助于推动可持续发展目标在高校层面的适应性调整。实验中表现最佳的模型为BART，其F1得分为0.786。