Publication: ClimRetrieve: A Benchmarking Dataset for Information Retrieval from Corporate Climate Disclosures
ClimRetrieve: A Benchmarking Dataset for Information Retrieval from Corporate Climate Disclosures
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Schimanski, T., Ni, J., Martín, R. S., Ranger, N., & Leippold, M. (2024). ClimRetrieve: A Benchmarking Dataset for Information Retrieval from Corporate Climate Disclosures. Proceedings of the Conference on Empirical Methods in Natural Language Processing, 17509–17524. https://doi.org/10.18653/v1/2024.emnlp-main.969
Abstract
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Abstract
To handle the vast amounts of qualitative data produced in corporate climate communication, stakeholders increasingly rely on Retrieval Augmented Generation (RAG) systems. However, a significant gap remains in evaluating domain-specific information retrieval – the basis for answer generation. To address this challenge, this work simulates the typical tasks of a sustainability analyst by examining 30 sustainability reports with 16 detailed climate-related questions. As a result, we obtain a dataset with over 8.5K unique question-source
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Schimanski, T., Ni, J., Martín, R. S., Ranger, N., & Leippold, M. (2024). ClimRetrieve: A Benchmarking Dataset for Information Retrieval from Corporate Climate Disclosures. Proceedings of the Conference on Empirical Methods in Natural Language Processing, 17509–17524. https://doi.org/10.18653/v1/2024.emnlp-main.969
