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

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.