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

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 pre...

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Vydáno v:Journal of data mining and digital humanities Ročník NLP4DH
Hlavní autoři: Kharlashkin, Lev, Macias, Melany, Huovinen, Leo, Hämäläinen, Mika
Médium: Journal Article
Jazyk:angličtina
Vydáno: INRIA 29.04.2024
Nicolas Turenne
Témata:
Computation and Language
Computer Science
computer science - computation and language
multi label classification
LLM
SDG multi label classification LLM
SDG
ISSN:2416-5999, 2416-5999
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Shrnutí: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.
ISSN:2416-5999
2416-5999
DOI:10.46298/jdmdh.13127