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...

Full description

Saved in:

Bibliographic Details
Published in:	Journal of data mining and digital humanities Vol. NLP4DH
Main Authors:	Kharlashkin, Lev, Macias, Melany, Huovinen, Leo, Hämäläinen, Mika
Format:	Journal Article
Language:	English
Published:	INRIA 29.04.2024 Nicolas Turenne
Subjects:	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 Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Description
Summary:	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