Nicolas Webersinke


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CHATREPORT: Democratizing Sustainability Disclosure Analysis through LLM-based Tools
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 | Markus Leippold
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

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: Github: Live web app:

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Environmental Claim Detection
Dominik Stammbach | Nicolas Webersinke | Julia Bingler | Mathias Kraus | Markus Leippold
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

To transition to a green economy, environmental claims made by companies must be reliable, comparable, and verifiable. To analyze such claims at scale, automated methods are needed to detect them in the first place. However, there exist no datasets or models for this. Thus, this paper introduces the task of environmental claim detection. To accompany the task, we release an expert-annotated dataset and models trained on this dataset. We preview one potential application of such models: We detect environmental claims made in quarterly earning calls and find that the number of environmental claims has steadily increased since the Paris Agreement in 2015.


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Towards Climate Awareness in NLP Research
Daniel Hershcovich | Nicolas Webersinke | Mathias Kraus | Julia Bingler | Markus Leippold
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

The climate impact of AI, and NLP research in particular, has become a serious issue given the enormous amount of energy that is increasingly being used for training and running computational models. Consequently, increasing focus is placed on efficient NLP. However, this important initiative lacks simple guidelines that would allow for systematic climate reporting of NLP research. We argue that this deficiency is one of the reasons why very few publications in NLP report key figures that would allow a more thorough examination of environmental impact, and present a quantitative survey to demonstrate this. As a remedy, we propose a climate performance model card with the primary purpose of being practically usable with only limited information about experiments and the underlying computer hardware. We describe why this step is essential to increase awareness about the environmental impact of NLP research and, thereby, paving the way for more thorough discussions.