Predicting Sustainable Development Goals Using Course Descriptions -- from LLMs to Conventional Foundation Models

Autor:	Kharlashkin, Lev, Macias, Melany, Huovinen, Leo, Hämäläinen, Mika
Rok vydání:	2024
Předmět:	Computer Science - Computation and Language
Zdroj:	Journal of Data Mining & Digital Humanities, NLP4DH (April 29, 2024) jdmdh:13127
Druh dokumentu:	Working Paper
DOI:	10.46298/jdmdh.13127
Popis:	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. Comment: 3 figures, 2 tables
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2402.16420 Zobrazit plný text záznamu View this record from Arxiv