Corporate Sustainability Analysis via Retrieval-Augmented Generation

Master Thesis (2024)
Author(s)

J.J. Jansen (TU Delft - Electrical Engineering, Mathematics and Computer Science)

Contributor(s)

Pradeep Murukannaiah – Mentor (TU Delft - Interactive Intelligence)

S. Mukherjee – Mentor (TU Delft - Interactive Intelligence)

Faculty
Electrical Engineering, Mathematics and Computer Science
More Info
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Publication Year
2024
Language
English
Graduation Date
01-07-2024
Awarding Institution
Delft University of Technology
Programme
Computer Science | Artificial Intelligence
Sponsors
None
Faculty
Electrical Engineering, Mathematics and Computer Science
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Abstract

We investigate the application of Retrieval-Augmented Generation (RAG) for enhancing the analysis of corporate sustainability disclosures. We introduce CorSus, a novel dataset for evaluating RAG models in answering corporate sustainability-focused claims, using data from the Transition Pathway Initiative for over 100 companies. Further, we develop a subset of this dataset with reference documentation and fully explained answers. Finally, in a systematic framework, we optimise and benchmark state-of-the-art RAG approaches using the CorSus dataset. With this work, we aim to empower stakeholders with a tool for informed evaluations of corporate sustainability practices, thereby encouraging a greater commitment to environmental responsibility.

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