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.

翻译：本文提出了一种新颖方法，通过将专家知识融入大语言模型（LLMs），以自动化分析企业可持续发展报告，并将其与气候相关财务信息披露工作组（TCFD）建议进行对标。企业可持续发展报告在评估组织的环境与社会风险及影响方面至关重要。然而，分析这些报告中的海量信息使得人工分析成本过高。因此，全球仅有少数实体拥有分析这些报告的资源，这可能导致透明度不足。尽管基于人工智能的工具能够自动分析数据，但由于缺乏领域特定专业知识，它们容易出现不准确之处。本文介绍了一种通过专家知识增强LLMs以自动化分析企业可持续发展报告的新方法。我们将该工具命名为CHATREPORT，并在首个应用案例中依据TCFD建议评估企业气候风险披露情况。CHATREPORT是气候科学、金融、经济政策与计算机科学领域专家合作的成果，展示了领域专家如何参与人工智能工具的开发。我们公开了提示模板、生成数据及评分，以促进透明度。