@inproceedings{wang-etal-2024-survey,
title = "A Survey on Natural Language Counterfactual Generation",
author = "Wang, Yongjie and
Qiu, Xiaoqi and
Yue, Yu and
Guo, Xu and
Zeng, Zhiwei and
Feng, Yuhong and
Shen, Zhiqi",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-emnlp.276",
pages = "4798--4818",
abstract = "Natural language counterfactual generation aims to minimally modify a given text such that the modified text will be classified into a different class. The generated counterfactuals provide insight into the reasoning behind a model{'}s predictions by highlighting which words significantly influence the outcomes. Additionally, they can be used to detect model fairness issues and augment the training data to enhance the model{'}s robustness. A substantial amount of research has been conducted to generate counterfactuals for various NLP tasks, employing different models and methodologies. With the rapid growth of studies in this field, a systematic review is crucial to guide future researchers and developers. To bridge this gap, this survey provides a comprehensive overview of textual counterfactual generation methods, particularly those based on Large Language Models. We propose a new taxonomy that systematically categorizes the generation methods into four groups and summarizes the metrics for evaluating the generation quality. Finally, we discuss ongoing research challenges and outline promising directions for future work.",
}
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%0 Conference Proceedings
%T A Survey on Natural Language Counterfactual Generation
%A Wang, Yongjie
%A Qiu, Xiaoqi
%A Yue, Yu
%A Guo, Xu
%A Zeng, Zhiwei
%A Feng, Yuhong
%A Shen, Zhiqi
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Findings of the Association for Computational Linguistics: EMNLP 2024
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F wang-etal-2024-survey
%X Natural language counterfactual generation aims to minimally modify a given text such that the modified text will be classified into a different class. The generated counterfactuals provide insight into the reasoning behind a model’s predictions by highlighting which words significantly influence the outcomes. Additionally, they can be used to detect model fairness issues and augment the training data to enhance the model’s robustness. A substantial amount of research has been conducted to generate counterfactuals for various NLP tasks, employing different models and methodologies. With the rapid growth of studies in this field, a systematic review is crucial to guide future researchers and developers. To bridge this gap, this survey provides a comprehensive overview of textual counterfactual generation methods, particularly those based on Large Language Models. We propose a new taxonomy that systematically categorizes the generation methods into four groups and summarizes the metrics for evaluating the generation quality. Finally, we discuss ongoing research challenges and outline promising directions for future work.
%U https://aclanthology.org/2024.findings-emnlp.276
%P 4798-4818
Markdown (Informal)
[A Survey on Natural Language Counterfactual Generation](https://aclanthology.org/2024.findings-emnlp.276) (Wang et al., Findings 2024)
ACL
- Yongjie Wang, Xiaoqi Qiu, Yu Yue, Xu Guo, Zhiwei Zeng, Yuhong Feng, and Zhiqi Shen. 2024. A Survey on Natural Language Counterfactual Generation. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 4798–4818, Miami, Florida, USA. Association for Computational Linguistics.