ESGReveal: An LLM-based approach for extracting structured data from ESG
  reports
Yi Zou Mengying Shi Zhongjie Chen Zhu Deng ZongXiong Lei Zihan Zeng Shiming Yang HongXiang Tong Lei Xiao Wenwen Zhou
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.