Leveraging Language Models to Detect Greenwashing
Fuente:
arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866915031810048000 |
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| author | Vinella, Avalon Capetz, Margaret Pattichis, Rebecca Chance, Christina Ghosh, Reshmi Chang, Kai-Wei |
| author_facet | Vinella, Avalon Capetz, Margaret Pattichis, Rebecca Chance, Christina Ghosh, Reshmi Chang, Kai-Wei |
| contents | In recent years, climate change repercussions have increasingly captured public interest. Consequently, corporations are emphasizing their environmental efforts in sustainability reports to bolster their public image. Yet, the absence of stringent regulations in review of such reports allows potential greenwashing. In this study, we introduce a novel preliminary methodology to train a language model on generated labels for greenwashing risk. Our primary contributions encompass: developing a preliminary mathematical formulation to quantify greenwashing risk, a fine-tuned ClimateBERT model for this problem, and a comparative analysis of results. On a test set comprising of sustainability reports, our best model achieved an average accuracy score of 86.34% and F1 score of 0.67, demonstrating that our proof-of-concept methodology shows a promising direction of exploration for this task. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_01469 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Leveraging Language Models to Detect Greenwashing Vinella, Avalon Capetz, Margaret Pattichis, Rebecca Chance, Christina Ghosh, Reshmi Chang, Kai-Wei Computation and Language Machine Learning In recent years, climate change repercussions have increasingly captured public interest. Consequently, corporations are emphasizing their environmental efforts in sustainability reports to bolster their public image. Yet, the absence of stringent regulations in review of such reports allows potential greenwashing. In this study, we introduce a novel preliminary methodology to train a language model on generated labels for greenwashing risk. Our primary contributions encompass: developing a preliminary mathematical formulation to quantify greenwashing risk, a fine-tuned ClimateBERT model for this problem, and a comparative analysis of results. On a test set comprising of sustainability reports, our best model achieved an average accuracy score of 86.34% and F1 score of 0.67, demonstrating that our proof-of-concept methodology shows a promising direction of exploration for this task. |
| title | Leveraging Language Models to Detect Greenwashing |
| topic | Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2311.01469 |