EulerESG: Automating ESG Disclosure Analysis with LLMs
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arXiv
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| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911289479004160 |
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| author | Ding, Yi Tang, Xushuo Yang, Zhengyi Zhang, Wenqian Wu, Simin Huang, Yuxin Lan, Lingjing Li, Weiyuan Chen, Yin Ju, Mingchen Yang, Wenke Hoang, Thong Klymenko, Mykhailo Zu, Xiwei Zhang, Wenjie |
| author_facet | Ding, Yi Tang, Xushuo Yang, Zhengyi Zhang, Wenqian Wu, Simin Huang, Yuxin Lan, Lingjing Li, Weiyuan Chen, Yin Ju, Mingchen Yang, Wenke Hoang, Thong Klymenko, Mykhailo Zu, Xiwei Zhang, Wenjie |
| contents | Environmental, Social, and Governance (ESG) reports have become central to how companies communicate climate risk, social impact, and governance practices, yet they are still published primarily as long, heterogeneous PDF documents. This makes it difficult to systematically answer seemingly simple questions. Existing tools either rely on brittle rule-based extraction or treat ESG reports as generic text, without explicitly modelling the underlying reporting standards. We present \textbf{EulerESG}, an LLM-powered system for automating ESG disclosure analysis with explicit awareness of ESG frameworks. EulerESG combines (i) dual-channel retrieval and LLM-driven disclosure analysis over ESG reports, and (ii) an interactive dashboard and chatbot for exploration, benchmarking, and explanation. Using four globally recognised companies and twelve SASB sub-industries, we show that EulerESG can automatically populate standard-aligned metric tables with high fidelity (up to 0.95 average accuracy) while remaining practical in end-to-end runtime, and we compare several recent LLM models in this setting. The full implementation, together with a demonstration video, is publicly available at https://github.com/UNSW-database/EulerESG. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_21712 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | EulerESG: Automating ESG Disclosure Analysis with LLMs Ding, Yi Tang, Xushuo Yang, Zhengyi Zhang, Wenqian Wu, Simin Huang, Yuxin Lan, Lingjing Li, Weiyuan Chen, Yin Ju, Mingchen Yang, Wenke Hoang, Thong Klymenko, Mykhailo Zu, Xiwei Zhang, Wenjie Computation and Language Artificial Intelligence Computers and Society Environmental, Social, and Governance (ESG) reports have become central to how companies communicate climate risk, social impact, and governance practices, yet they are still published primarily as long, heterogeneous PDF documents. This makes it difficult to systematically answer seemingly simple questions. Existing tools either rely on brittle rule-based extraction or treat ESG reports as generic text, without explicitly modelling the underlying reporting standards. We present \textbf{EulerESG}, an LLM-powered system for automating ESG disclosure analysis with explicit awareness of ESG frameworks. EulerESG combines (i) dual-channel retrieval and LLM-driven disclosure analysis over ESG reports, and (ii) an interactive dashboard and chatbot for exploration, benchmarking, and explanation. Using four globally recognised companies and twelve SASB sub-industries, we show that EulerESG can automatically populate standard-aligned metric tables with high fidelity (up to 0.95 average accuracy) while remaining practical in end-to-end runtime, and we compare several recent LLM models in this setting. The full implementation, together with a demonstration video, is publicly available at https://github.com/UNSW-database/EulerESG. |
| title | EulerESG: Automating ESG Disclosure Analysis with LLMs |
| topic | Computation and Language Artificial Intelligence Computers and Society |
| url | https://arxiv.org/abs/2511.21712 |