A Survey on Natural Language Counterfactual Generation

Yongjie Wang, Xiaoqi Qiu, Yu Yue, Xu Guo, Zhiwei Zeng, Yuhong Feng, Zhiqi Shen

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.
Anthology ID:
2024.findings-emnlp.276
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2024
Month:
November
Year:
2024
Address:
Miami, Florida, USA
Editors:
Yaser Al-Onaizan, Mohit Bansal, Yun-Nung Chen
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4798–4818
Language:
URL:
https://preview.aclanthology.org/build-pipeline-with-new-library/2024.findings-emnlp.276/
DOI:
10.18653/v1/2024.findings-emnlp.276
Bibkey:
Cite (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.
Cite (Informal):
A Survey on Natural Language Counterfactual Generation (Wang et al., Findings 2024)
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PDF:
https://preview.aclanthology.org/build-pipeline-with-new-library/2024.findings-emnlp.276.pdf
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