@inproceedings{ahmed-etal-2025-enhancing,
title = "Enhancing Retrieval for {ESGLLM} via {ESG}-{CID}: A Disclosure Content Index Finetuning Dataset for Mapping {GRI} and {ESRS}",
author = "Ahmed, Shafiuddin Rehan and
Shah, Ankit and
Tran, Quan Hung and
Khetan, Vivek and
Kang, Sukryool and
Mehta, Ankit and
Bao, Yujia and
Wei, Wei",
editor = "Dutia, Kalyan and
Henderson, Peter and
Leippold, Markus and
Manning, Christoper and
Morio, Gaku and
Muccione, Veruska and
Ni, Jingwei and
Schimanski, Tobias and
Stammbach, Dominik and
Singh, Alok and
Su, Alba (Ruiran) and
A. Vaghefi, Saeid",
booktitle = "Proceedings of the 2nd Workshop on Natural Language Processing Meets Climate Change (ClimateNLP 2025)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.climatenlp-1.1/",
doi = "10.18653/v1/2025.climatenlp-1.1",
pages = "1--16",
ISBN = "979-8-89176-259-6",
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."
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<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>
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%0 Conference Proceedings
%T Enhancing Retrieval for ESGLLM via ESG-CID: A Disclosure Content Index Finetuning Dataset for Mapping GRI and ESRS
%A Ahmed, Shafiuddin Rehan
%A Shah, Ankit
%A Tran, Quan Hung
%A Khetan, Vivek
%A Kang, Sukryool
%A Mehta, Ankit
%A Bao, Yujia
%A Wei, Wei
%Y Dutia, Kalyan
%Y Henderson, Peter
%Y Leippold, Markus
%Y Manning, Christoper
%Y Morio, Gaku
%Y Muccione, Veruska
%Y Ni, Jingwei
%Y Schimanski, Tobias
%Y Stammbach, Dominik
%Y Singh, Alok
%Y Su, Alba (Ruiran)
%Y A. Vaghefi, Saeid
%S Proceedings of the 2nd Workshop on Natural Language Processing Meets Climate Change (ClimateNLP 2025)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-259-6
%F ahmed-etal-2025-enhancing
%X 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.
%R 10.18653/v1/2025.climatenlp-1.1
%U https://aclanthology.org/2025.climatenlp-1.1/
%U https://doi.org/10.18653/v1/2025.climatenlp-1.1
%P 1-16
Markdown (Informal)
[Enhancing Retrieval for ESGLLM via ESG-CID: A Disclosure Content Index Finetuning Dataset for Mapping GRI and ESRS](https://aclanthology.org/2025.climatenlp-1.1/) (Ahmed et al., ClimateNLP 2025)
ACL
- Shafiuddin Rehan Ahmed, Ankit Shah, Quan Hung Tran, Vivek Khetan, Sukryool Kang, Ankit Mehta, Yujia Bao, and Wei Wei. 2025. Enhancing Retrieval for ESGLLM via ESG-CID: A Disclosure Content Index Finetuning Dataset for Mapping GRI and ESRS. In Proceedings of the 2nd Workshop on Natural Language Processing Meets Climate Change (ClimateNLP 2025), pages 1–16, Vienna, Austria. Association for Computational Linguistics.