Predicting Sustainable Development Goals Using Course Descriptions --
  from LLMs to Conventional Foundation Models
Lev Kharlashkin Melany Macias Leo Huovinen Mika Hämäläinen
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.