Publication: CHATREPORT: Democratizing Sustainability Disclosure Analysis through LLM-based Tools
CHATREPORT: Democratizing Sustainability Disclosure Analysis through LLM-based Tools
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Ni, J., Bingler, J., Colesanti Senni, C., Kraus, M., Gostlow, G., Schimanski, T., Stammbach, D., Ashraf Vaghefi, S., Wang, Q., Webersinke, N., Wekhof, T., Yu, T., & Leippold, M. (2023). CHATREPORT: Democratizing Sustainability Disclosure Analysis through LLM-based Tools. Proceedings of the Conference on Empirical Methods in Natural Language Processing, 21–51. https://doi.org/10.18653/v1/2023.emnlp-demo.3
Abstract
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In the face of climate change, are companies really taking substantial steps toward more sustainable operations? A comprehensive answer lies in the dense, information-rich landscape of corporate sustainability reports. However, the sheer volume and complexity of these reports make human analysis very costly. Therefore, only a few entities worldwide have the resources to analyze these reports at scale, which leads to a lack of transparency in sustainability reporting. Empowering stakeholders with LLM-based automatic analysis tools can
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Creators (Authors)
- Ni, Jingwei
- Bingler, Julia
- Kraus, Mathias
- Gostlow, Glen
- Schimanski, Tobias
- Stammbach, Dominik
- Ashraf Vaghefi, Saeid
- Wang, Qian
- Webersinke, Nicolas
- Wekhof, Tobias
- Yu, Tingyu
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Ni, J., Bingler, J., Colesanti Senni, C., Kraus, M., Gostlow, G., Schimanski, T., Stammbach, D., Ashraf Vaghefi, S., Wang, Q., Webersinke, N., Wekhof, T., Yu, T., & Leippold, M. (2023). CHATREPORT: Democratizing Sustainability Disclosure Analysis through LLM-based Tools. Proceedings of the Conference on Empirical Methods in Natural Language Processing, 21–51. https://doi.org/10.18653/v1/2023.emnlp-demo.3
