@inproceedings{ni-etal-2023-chatreport,
title = "{CHATREPORT}: Democratizing Sustainability Disclosure Analysis through {LLM}-based Tools",
author = "Ni, Jingwei and
Bingler, Julia and
Colesanti-Senni, Chiara and
Kraus, Mathias and
Gostlow, Glen and
Schimanski, Tobias and
Stammbach, Dominik and
Ashraf Vaghefi, Saeid and
Wang, Qian and
Webersinke, Nicolas and
Wekhof, Tobias and
Yu, Tingyu and
Leippold, Markus",
editor = "Feng, Yansong and
Lefever, Els",
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.emnlp-demo.3",
doi = "10.18653/v1/2023.emnlp-demo.3",
pages = "21--51",
abstract = "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 be a promising way to democratize sustainability report analysis. However, developing such tools is challenging due to (1) the hallucination of LLMs and (2) the inefficiency of bringing domain experts into the AI development loop. In this paper, we introduce ChatReport, a novel LLM-based system to automate the analysis of corporate sustainability reports, addressing existing challenges by (1) making the answers traceable to reduce the harm of hallucination and (2) actively involving domain experts in the development loop. We make our methodology, annotated datasets, and generated analyses of 1015 reports publicly available. Video Introduction: \url{https://www.youtube.com/watch?v=Q5AzaKzPE4M} Github: \url{https://github.com/EdisonNi-hku/chatreport} Live web app: reports.chatclimate.ai",
}
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<abstract>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 be a promising way to democratize sustainability report analysis. However, developing such tools is challenging due to (1) the hallucination of LLMs and (2) the inefficiency of bringing domain experts into the AI development loop. In this paper, we introduce ChatReport, a novel LLM-based system to automate the analysis of corporate sustainability reports, addressing existing challenges by (1) making the answers traceable to reduce the harm of hallucination and (2) actively involving domain experts in the development loop. We make our methodology, annotated datasets, and generated analyses of 1015 reports publicly available. Video Introduction: https://www.youtube.com/watch?v=Q5AzaKzPE4M Github: https://github.com/EdisonNi-hku/chatreport Live web app: reports.chatclimate.ai</abstract>
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%0 Conference Proceedings
%T CHATREPORT: Democratizing Sustainability Disclosure Analysis through LLM-based Tools
%A Ni, Jingwei
%A Bingler, Julia
%A Colesanti-Senni, Chiara
%A Kraus, Mathias
%A Gostlow, Glen
%A Schimanski, Tobias
%A Stammbach, Dominik
%A Ashraf Vaghefi, Saeid
%A Wang, Qian
%A Webersinke, Nicolas
%A Wekhof, Tobias
%A Yu, Tingyu
%A Leippold, Markus
%Y Feng, Yansong
%Y Lefever, Els
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F ni-etal-2023-chatreport
%X 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 be a promising way to democratize sustainability report analysis. However, developing such tools is challenging due to (1) the hallucination of LLMs and (2) the inefficiency of bringing domain experts into the AI development loop. In this paper, we introduce ChatReport, a novel LLM-based system to automate the analysis of corporate sustainability reports, addressing existing challenges by (1) making the answers traceable to reduce the harm of hallucination and (2) actively involving domain experts in the development loop. We make our methodology, annotated datasets, and generated analyses of 1015 reports publicly available. Video Introduction: https://www.youtube.com/watch?v=Q5AzaKzPE4M Github: https://github.com/EdisonNi-hku/chatreport Live web app: reports.chatclimate.ai
%R 10.18653/v1/2023.emnlp-demo.3
%U https://aclanthology.org/2023.emnlp-demo.3
%U https://doi.org/10.18653/v1/2023.emnlp-demo.3
%P 21-51
Markdown (Informal)
[CHATREPORT: Democratizing Sustainability Disclosure Analysis through LLM-based Tools](https://aclanthology.org/2023.emnlp-demo.3) (Ni et al., EMNLP 2023)
ACL
- Jingwei Ni, Julia Bingler, Chiara Colesanti-Senni, Mathias Kraus, Glen Gostlow, Tobias Schimanski, Dominik Stammbach, Saeid Ashraf Vaghefi, Qian Wang, Nicolas Webersinke, Tobias Wekhof, Tingyu Yu, and Markus Leippold. 2023. CHATREPORT: Democratizing Sustainability Disclosure Analysis through LLM-based Tools. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 21–51, Singapore. Association for Computational Linguistics.