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...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Journal of data mining and digital humanities Jg. NLP4DH
Hauptverfasser: Kharlashkin, Lev, Macias, Melany, Huovinen, Leo, Hämäläinen, Mika
Format: Journal Article
Sprache:Englisch
Veröffentlicht: INRIA 29.04.2024
Nicolas Turenne
Schlagworte:
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
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung: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