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ClimRetrieve: A Benchmarking Dataset for Information Retrieval from Corporate Climate Disclosures

Author

Listed:
  • Tobias Schimanski

    (University of Zurich)

  • Jingwei Ni

    (ETH Zurich)

  • Roberto Spacey

    (University of Oxford)

  • Nicola Ranger

    (Environmental Change Institute, University of Oxford)

  • Markus Leippold

    (University of Zurich; Swiss Finance Institute)

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-answer pairs labeled by different levels of relevance. Furthermore, we develop a use case with the dataset to investigate the integration of expert knowledge into information retrieval with embeddings. Although we show that incorporating expert knowledge works, we also outline the critical limitations of embeddings in knowledge-intensive downstream domains like climate change communication.

Suggested Citation

  • Tobias Schimanski & Jingwei Ni & Roberto Spacey & Nicola Ranger & Markus Leippold, 2024. "ClimRetrieve: A Benchmarking Dataset for Information Retrieval from Corporate Climate Disclosures," Swiss Finance Institute Research Paper Series 24-89, Swiss Finance Institute.
    • Handle: RePEc:chf:rpseri:rp2489
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