  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
christen our tool CHATREPORT, and apply it in a first use case to assess
corporate climate risk disclosures following the TCFD recommendations.
CHATREPORT results from collaborating with experts in climate science, finance,
economic policy, and computer science, demonstrating how domain experts can be
involved in developing AI tools. We make our prompt templates, generated data,
and scores available to the public to encourage transparency.
