Paradigm Shift in Sustainability Disclosure Analysis: Empowering Stakeholders with CHATREPORT, a Language Model-Based Tool
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

TL;DR
This paper presents CHATREPORT, a language model-based tool enhanced with expert knowledge to automate and improve the analysis of corporate sustainability reports against TCFD standards, promoting transparency and efficiency.
Contribution
The paper introduces CHATREPORT, a novel LLM-based tool integrated with domain expertise to automate sustainability report analysis, addressing accuracy and resource limitations.
Findings
CHATREPORT effectively assesses climate risk disclosures
The tool demonstrates improved accuracy over generic LLMs
Promotes transparency by sharing prompts and data publicly
Abstract
This paper introduces a novel approach to enhance Large Language Models (LLMs) with expert knowledge to automate the analysis of corporate sustainability reports by benchmarking them against the Task Force for Climate-Related Financial Disclosures (TCFD) recommendations. Corporate sustainability reports are crucial in assessing organizations' environmental and social risks and impacts. However, analyzing these reports' vast amounts of information makes human analysis often too costly. As a result, only a few entities worldwide have the resources to analyze these reports, which could lead to a lack of transparency. While AI-powered tools can automatically analyze the data, they are prone to inaccuracies as they lack domain-specific expertise. This paper introduces a novel approach to enhance LLMs with expert knowledge to automate the analysis of corporate sustainability reports. We…
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Taxonomy
TopicsCorporate Social Responsibility Reporting · Sustainable Finance and Green Bonds · Impact of AI and Big Data on Business and Society
