Abstract
Climate change has intensified the need for transparency and accountability in organizational practices, making Environmental, Social, and Governance (ESG) reporting increasingly crucial. Frameworks like the Global Reporting Initiative (GRI) and the new European Sustainability Reporting Standards (ESRS) aim to standardize ESG reporting, yet generating comprehensive reports remains challenging due to the considerable length of ESG documents and variability in company reporting styles. To facilitate ESG report automation, Retrieval-Augmented Generation (RAG) systems can be employed, but their development is hindered by a lack of labeled data suitable for training retrieval models. In this paper, we leverage an underutilized source of weak supervision -- the disclosure content index found in past ESG reports -- to create a comprehensive dataset, ESG-CID, for both GRI and ESRS standards. By extracting mappings between specific disclosure requirements and corresponding report sections, and refining them using a Large Language Model as a judge, we generate a robust training and evaluation set. We benchmark popular embedding models on this dataset and show that fine-tuning BERT-based models can outperform commercial embeddings and leading public models, even under temporal data splits for cross-report style transfer from GRI to ESRS
Abstract (translated)
气候变化加剧了组织实践中透明度和问责制的需求，使得环境、社会和治理（ESG）报告变得愈加重要。全球报告倡议（Global Reporting Initiative, GRI）和新的欧洲可持续性报告标准（European Sustainability Reporting Standards, ESRS）等框架旨在标准化ESG报告的编制，然而由于ESG文件篇幅较长以及公司报告风格的变化多端，编写全面的ESG报告仍然具有挑战性。为了促进ESG报告自动化，可以采用检索增强生成（Retrieval-Augmented Generation, RAG）系统，但这类系统的开发因缺乏适合训练检索模型的标注数据而受限。 在本文中，我们利用了一个被忽视的弱监督来源——即过去ESG报告中的披露内容索引，创建了适用于GRI和ESRS标准的综合性数据集“ESG-CID”。通过提取特定披露要求与相应报告部分之间的映射，并使用大型语言模型进行校准，我们生成了一套稳健的训练及评估数据。我们在该数据集上对流行嵌入式模型进行了基准测试，并展示了通过对BERT基线模型微调，可以超越商业嵌入和领先公共模型的性能表现，即使是在跨报告风格转换（从GRI到ESRS）的时间分割数据下也是如此。 综上所述，本研究不仅贡献了一个全面的ESG披露索引数据集“ESG-CID”，还展示了通过大型语言模型指导下的微调过程可以有效提升检索增强生成系统的能力，在解决ESG自动化报告编制方面的挑战时展现出了巨大潜力。
URL
https://arxiv.org/abs/2503.10674
