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: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
INRIA
29.04.2024
Nicolas Turenne |
| Témata: | |
| ISSN: | 2416-5999, 2416-5999 |
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| Abstract | 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. |
|---|---|
| AbstractList | 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. 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. |
| Author | Kharlashkin, Lev Hämäläinen, Mika Macias, Melany Huovinen, Leo |
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| Keywords | multi label classification LLM SDG multi label classification LLM SDG |
| Language | English |
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| Snippet | 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... 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... |
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| Title | Predicting Sustainable Development Goals Using Course Descriptions -- from LLMs to Conventional Foundation Models |
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