ESG Classification by Implicit Rule Learning via GPT-4

Yun Hyojeong, Kim Chanyoung, Moonjeong Hahm, Kyuri Kim, Guijin Son


Abstract
In this work, we adopt multiple prompting, chain-of-thought reasoning, and in-context learning strategies to guide GPT-4 in solving ESG classification tasks. We rank second in the Korean subset for Shared Task ML-ESG-3 in Impact Type prediction. Furthermore, we adopt open models to explain their calibration and robustness to different prompting strategies. The longer general pre-training correlates with enhanced performance in financial downstream tasks.
Anthology ID:
2024.finnlp-1.28
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:
261–268
Language:
URL:
https://aclanthology.org/2024.finnlp-1.28
DOI:
Bibkey:
Cite (ACL):
Yun Hyojeong, Kim Chanyoung, Moonjeong Hahm, Kyuri Kim, and Guijin Son. 2024. ESG Classification by Implicit Rule Learning via GPT-4. 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 261–268, Torino, Italia. Association for Computational Linguistics.
Cite (Informal):
ESG Classification by Implicit Rule Learning via GPT-4 (Hyojeong et al., FinNLP 2024)
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PDF:
https://preview.aclanthology.org/nschneid-patch-4/2024.finnlp-1.28.pdf