Software and data underlying the publication: "Natural Language Counterfactual Explanations in Financial Text Classification: A Comparison of Generators and Evaluation Metrics"

DOI:10.4121/7270e8b5-134a-4939-b614-158a7d225622.v1
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DOI: 10.4121/7270e8b5-134a-4939-b614-158a7d225622

Datacite citation style

Dobiczek, Karol; Altmeyer, Patrick; Liem, Cynthia (2025): Software and data underlying the publication: "Natural Language Counterfactual Explanations in Financial Text Classification: A Comparison of Generators and Evaluation Metrics". Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/7270e8b5-134a-4939-b614-158a7d225622.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite

Dataset

This dataset contains the data collected through experiments, surveys, and analyzed results obtained for the ACL GEM^2 2025 workshop submission titled Natural Language Counterfactual Explanations in Financial Text Classification: A Comparison of Generators and Evaluation Metrics. This project aimed to use texts from expert domains in order to evaluate state-of-the-art methods for generating text counterfactual explanations for large language model text classification. The data contains pre-processed texts from a financial dataset "Trillion Dollar Words", the counterfactuals generated in the experiments, as well raw and pre-processed results of the metric-based and human annotation-based experiments. Additionally, we include the software used in generating our results.

History

  • 2025-11-18 first online, published, posted

Publisher

4TU.ResearchData

Format

.zip compressed catalogs containing .csv and .json data files, and .ipynb software files

Organizations

TU Delft, Faculty of Electrical Engineering, Mathematics and Computer Science, Department of Intelligent Systems

DATA

Files (2)