@inproceedings{wang-etal-2025-fitcf,
title = "{F}it{CF}: A Framework for Automatic Feature Importance-guided Counterfactual Example Generation",
author = {Wang, Qianli and
Feldhus, Nils and
Ostermann, Simon and
Villa-Arenas, Luis Felipe and
M{\"o}ller, Sebastian and
Schmitt, Vera},
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.64/",
doi = "10.18653/v1/2025.findings-acl.64",
pages = "1176--1191",
ISBN = "979-8-89176-256-5",
abstract = "Counterfactual examples are widely used in natural language processing (NLP) as valuable data to improve models, and in explainable artificial intelligence (XAI) to understand model behavior. The automated generation of counterfactual examples remains a challenging task even for large language models (LLMs), despite their impressive performance on many tasks. In this paper, we first introduce ZeroCF, a faithful approach for leveraging important words derived from feature attribution methods to generate counterfactual examples in a zero-shot setting. Second, we present a new framework, FitCF, which further verifies aforementioned counterfactuals by label flip verification and then inserts them as demonstrations for few-shot prompting, outperforming three state-of-the-art baselines. Through ablation studies, we identify the importance of each of FitCF{'}s core components in improving the quality of counterfactuals, as assessed through flip rate, perplexity, and similarity measures. Furthermore, we show the effectiveness of LIME and Integrated Gradients as backbone attribution methods for FitCF and find that the number of demonstrations has the largest effect on performance. Finally, we reveal a strong correlation between the faithfulness of feature attribution scores and the quality of generated counterfactuals, which we hope will serve as an importantfinding for future research in this direction."
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<abstract>Counterfactual examples are widely used in natural language processing (NLP) as valuable data to improve models, and in explainable artificial intelligence (XAI) to understand model behavior. The automated generation of counterfactual examples remains a challenging task even for large language models (LLMs), despite their impressive performance on many tasks. In this paper, we first introduce ZeroCF, a faithful approach for leveraging important words derived from feature attribution methods to generate counterfactual examples in a zero-shot setting. Second, we present a new framework, FitCF, which further verifies aforementioned counterfactuals by label flip verification and then inserts them as demonstrations for few-shot prompting, outperforming three state-of-the-art baselines. Through ablation studies, we identify the importance of each of FitCF’s core components in improving the quality of counterfactuals, as assessed through flip rate, perplexity, and similarity measures. Furthermore, we show the effectiveness of LIME and Integrated Gradients as backbone attribution methods for FitCF and find that the number of demonstrations has the largest effect on performance. Finally, we reveal a strong correlation between the faithfulness of feature attribution scores and the quality of generated counterfactuals, which we hope will serve as an importantfinding for future research in this direction.</abstract>
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%0 Conference Proceedings
%T FitCF: A Framework for Automatic Feature Importance-guided Counterfactual Example Generation
%A Wang, Qianli
%A Feldhus, Nils
%A Ostermann, Simon
%A Villa-Arenas, Luis Felipe
%A Möller, Sebastian
%A Schmitt, Vera
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F wang-etal-2025-fitcf
%X Counterfactual examples are widely used in natural language processing (NLP) as valuable data to improve models, and in explainable artificial intelligence (XAI) to understand model behavior. The automated generation of counterfactual examples remains a challenging task even for large language models (LLMs), despite their impressive performance on many tasks. In this paper, we first introduce ZeroCF, a faithful approach for leveraging important words derived from feature attribution methods to generate counterfactual examples in a zero-shot setting. Second, we present a new framework, FitCF, which further verifies aforementioned counterfactuals by label flip verification and then inserts them as demonstrations for few-shot prompting, outperforming three state-of-the-art baselines. Through ablation studies, we identify the importance of each of FitCF’s core components in improving the quality of counterfactuals, as assessed through flip rate, perplexity, and similarity measures. Furthermore, we show the effectiveness of LIME and Integrated Gradients as backbone attribution methods for FitCF and find that the number of demonstrations has the largest effect on performance. Finally, we reveal a strong correlation between the faithfulness of feature attribution scores and the quality of generated counterfactuals, which we hope will serve as an importantfinding for future research in this direction.
%R 10.18653/v1/2025.findings-acl.64
%U https://aclanthology.org/2025.findings-acl.64/
%U https://doi.org/10.18653/v1/2025.findings-acl.64
%P 1176-1191
Markdown (Informal)
[FitCF: A Framework for Automatic Feature Importance-guided Counterfactual Example Generation](https://aclanthology.org/2025.findings-acl.64/) (Wang et al., Findings 2025)
ACL