ESG company proiling, a Retrieval-Augmented Generation (RAG): Approach for data-driven corporate analysis
Abstract
This Master’s thesis addresses the challenge of creating comprehensive fact-based ESG (Environmental, Social, and Governance) company profiles, a procedure obstructed by the manual synthesis of diverse, often unstructured, data, and the limitations of standard Large Language Models (LLMs) in ensuring accuracy and traceability. The project proposes and implements a Retrieval-Augmented Generation (RAG) framework designed to automate the initial drafting of ESG profiles while maintaining high factual integrity and source attribution. The implemented methodology involves the ingestion of external documents (such as sustainability and research reports) and proprietary internal ESG metrics. External documents undergo pre-processing, including robust parsing and chunking, before being vectorized and stored in a knowledge base. The core RAG pipeline dynamically retrieves relevant document pages based on the specific ESG topic being analyzed, incorporates formatted internal data, and uses sophisticated prompt engineering to guide a generative LLM. A crucial post-generation verification layer uses a separate LLM to automatically assess factual accuracy and citation correctness against sources. Evaluation of the implemented system has shown significant efficiency gains, reducing the time required for analysts to produce a final ESG company profile by approximately 80% compared to traditional manual methods. The automated verification layer has successfully identified inconsistencies…
Read the paper
Related studies
- Knowledge Management Perspective of Generative Artificial Intelligence
- Navigating Generative Artificial Intelligence Promises and Perils for Knowledge and Creative Work
- Characterizing the evolving research on enterprise content management
- Large language models and multimodal foundation models for precision oncology
- From Generative AI to Extended Reality: Multidisciplinary Perspectives on the Challenges, Opportunities, and Future of Educational Computing
- Generative Artificial Intelligence: A New Engine for Advancing Environmental Science and Engineering
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.