Abstract
Since the United Nations defined the Sustainable Development Goals, studies have shown that these goals are interlinked in different ways. The concept of SDG interlinkages refers to the complex network of interactions existing within and between the SDGs themselves. These interactions are referred to as synergies and trade-offs. Synergies represent positive interactions where the progress of one SDG contributes positively to the progress of another. On the other hand, trade-offs are negative interactions where the progress of one SDG has a negative impact on another. However, evaluating such interlinkages is a complex task, not only because of the multidimensional nature of SDGs, but also because it is highly exposed to personal interpretation bias and technical limitations. Recent studies are mainly based on expert judgements, literature reviews, sentiment or data analysis. To remedy these limitations we propose the use of Small Language Models in addition of an advanced Retrieval Augmented Generation to distinguish synergies and trade-offs between SDGs. In order to validate our results, we have drawn on the study carried out by the European Commission’s Joint Research Centre which provides a database of interlinkages labelled according to the presence of synergies or trade-offs.- Anthology ID:
- 2024.finnlp-1.3
- Volume:
- Proceedings of the Joint Workshop of the 7th Financial Technology and Natural Language Processing, the 5th Knowledge Discovery from Unstructured Data in Financial Services, and the 4th Workshop on Economics and Natural Language Processing
- Month:
- May
- Year:
- 2024
- Address:
- Torino, Italia
- Editors:
- Chung-Chi Chen, Xiaomo Liu, Udo Hahn, Armineh Nourbakhsh, Zhiqiang Ma, Charese Smiley, Veronique Hoste, Sanjiv Ranjan Das, Manling Li, Mohammad Ghassemi, Hen-Hsen Huang, Hiroya Takamura, Hsin-Hsi Chen
- Venue:
- FinNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 21–33
- Language:
- URL:
- https://aclanthology.org/2024.finnlp-1.3
- DOI:
- Cite (ACL):
- Loris Bergeron, Jerome Francois, Radu State, and Jean Hilger. 2024. BLU-SynTra: Distinguish Synergies and Trade-offs between Sustainable Development Goals Using Small Language Models. In Proceedings of the Joint Workshop of the 7th Financial Technology and Natural Language Processing, the 5th Knowledge Discovery from Unstructured Data in Financial Services, and the 4th Workshop on Economics and Natural Language Processing, pages 21–33, Torino, Italia. Association for Computational Linguistics.
- Cite (Informal):
- BLU-SynTra: Distinguish Synergies and Trade-offs between Sustainable Development Goals Using Small Language Models (Bergeron et al., FinNLP 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-5/2024.finnlp-1.3.pdf