ESGReveal is an innovative method proposed for efficiently extracting and
analyzing Environmental, Social, and Governance (ESG) data from corporate
reports, catering to the critical need for reliable ESG information retrieval.
This approach utilizes Large Language Models (LLM) enhanced with Retrieval
Augmented Generation (RAG) techniques. The ESGReveal system includes an ESG
metadata module for targeted queries, a preprocessing module for assembling
databases, and an LLM agent for data extraction. Its efficacy was appraised
using ESG reports from 166 companies across various sectors listed on the Hong
Kong Stock Exchange in 2022, ensuring comprehensive industry and market
capitalization representation. Utilizing ESGReveal unearthed significant
insights into ESG reporting with GPT-4, demonstrating an accuracy of 76.9% in
data extraction and 83.7% in disclosure analysis, which is an improvement over
baseline models. This highlights the framework's capacity to refine ESG data
analysis precision. Moreover, it revealed a demand for reinforced ESG
disclosures, 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, a
functionality intended for future enhancement, the study calls for continued
research to further develop and compare the analytical capabilities of various
LLMs. In summary, ESGReveal is a stride forward in ESG data processing,
offering stakeholders a sophisticated tool to better evaluate and advance
corporate sustainability efforts. Its evolution is promising in promoting
transparency in corporate reporting and aligning with broader sustainable
development aims.

ESGReveal 是一种创新方法，利用大型语言模型（LLM）结合检索增强生成（RAG）技术高效提取和分析企业报告中的环境、社会和治理（ESG）数据，满足可靠的 ESG 信息检索的紧迫需求。它能够精确提取数据并分析披露，为企业可持续发展提供透明度和准确性，表明在 ESG 数据处理方面有促进透明度和实现可持续发展目标的潜力。