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.
@article{arxiv.2406.09818,
title = {ClimRetrieve: A Benchmarking Dataset for Information Retrieval from Corporate Climate Disclosures},
author = {Tobias Schimanski and Jingwei Ni and Roberto Spacey and Nicola Ranger and Markus Leippold},
journal= {arXiv preprint arXiv:2406.09818},
year = {2024}
}