Work

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

Year: 2024

Type: preprint

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.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2406.09818

Authors: Schimanski, Tobias, Ni, Jingwei, Spacey, Roberto, Ranger, Nicola, Leippold, Markus

Institutions: University of Zurich, ETH Zurich, University of Oxford, Swiss Finance Institute

Language: English

Topic: Topic Modeling

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Climate action

Open Access status: green

Funders: National Science Foundation, Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung