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ESGReveal is an innovative method proposed for efficiently extracting andanalyzing Environmental, Social, and Governance (ESG) data from corporatereports, catering to the critical need for reliable ESG information retrieval.This approach utilizes Large Language Models (LLM) enhanced with RetrievalAugmented Generation (RAG) techniques. The ESGReveal system includes an ESGmetadata module for targeted queries, a preprocessing module for assemblingdatabases, and an LLM agent for data extraction. Its efficacy was appraisedusing ESG reports from 166 companies across various sectors listed on the HongKong Stock Exchange in 2022, ensuring comprehensive industry and marketcapitalization representation. Utilizing ESGReveal unearthed significantinsights into ESG reporting with GPT-4, demonstrating an accuracy of 76.9% indata extraction and 83.7% in disclosure analysis, which is an improvement overbaseline models. This highlights the framework's capacity to refine ESG dataanalysis precision. Moreover, it revealed a demand for reinforced ESGdisclosures, with environmental and social data disclosures standing at 69.5%and 57.2%, respectively, suggesting a pursuit for more corporate transparency.While current iterations of ESGReveal do not process pictorial information, afunctionality intended for future enhancement, the study calls for continuedresearch to further develop and compare the analytical capabilities of variousLLMs. In summary, ESGReveal is a stride forward in ESG data processing,offering stakeholders a sophisticated tool to better evaluate and advancecorporate sustainability efforts. Its evolution is promising in promotingtransparency in corporate reporting and aligning with broader sustainabledevelopment aims.

ESGReveal: An LLM-based approach for extracting structured data from ESG reports