自然语言反事实生成旨在对给定文本进行最小程度的修改，使修改后的文本被划分至不同类别。生成的反事实样本通过凸显对结果有显著影响的词汇，揭示模型预测背后的推理逻辑。此外，它们可用于检测模型的公平性问题，还能扩充训练数据以提升模型的鲁棒性。目前已有大量研究针对各类自然语言处理（NLP）任务生成反事实样本，采用了不同的模型与方法。随着该领域研究的快速增长，开展系统性综述对指导未来的研究者和开发者至关重要。为填补这一空白，本综述全面梳理了文本反事实生成方法，尤其是基于大语言模型（Large Language Models）的方法。我们提出了一种新的分类体系，将生成方法系统地分为四类，并总结了评估生成质量的指标。最后，我们讨论了当前的研究挑战，并勾勒出未来工作的有前景方向。
英文摘要：
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.
Counterfactual Debating with Preset Stances for Hallucination Elimination of LLMs
University of Science and Technology of China（中国科学技术大学）；National University of Singapore（新加坡国立大学）；Electronic Science Research Institute of China Electronics（中国电子科技集团电子科学研究院）
Measuring and Improving Attentiveness to Partial Inputs with Counterfactuals
Allen Institute for AI（艾伦人工智能研究所）；University of Washington（华盛顿大学）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；University of Utah（犹他大学）；University of California, Irvine（加利福尼亚大学欧文分校）
FitCF: A Framework for Automatic Feature Importance-guided Counterfactual Example Generation
Quality and Usability Lab, Technische Universität Berlin（柏林技术大学质量与可用性实验室）；German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心）；Deutsche Telekom（德国电信）；Saarland Informatics Campus（萨尔州信息学校区）；Centre for European Research in Trusted AI (CERTAIN)（可信人工智能欧洲研究中心）；BIFOLD – Berlin Institute for the Foundations of Learning and Data（柏林学习与数据基础研究院）
CLOMO: Counterfactual Logical Modification with Large Language Models
City University of Hong Kong（香港城市大学）；Tsinghua University（清华大学）；Tencent AI Lab（腾讯AI实验室）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；City University of Hong Kong Shenzhen Research Institute（香港城市大学深圳研究院）；Shenzhen Campus of Sun Yat-sen University（中山大学深圳校区）；MBZUAI（Mohamed bin Zayed 人工智能大学）；DarkMatter AI Research（DarkMatter AI 研究院）
RewardExplainer: Learning Reward Model Explanations from Counterfactual Preference Feedback
The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Shanghai Jiao Tong University（上海交通大学）；Carnegie Mellon University（卡内基梅隆大学）；Jilin University（吉林大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）
DeepImagine: Clinical Trial Outcome Prediction via Stepwise Local Counterfactual Imaginations
Laboratory for Emerging Intelligence, University of California, San Diego（新兴智能实验室，加州大学圣地亚哥分校）；Department of Dermatology, University of California, San Diego（皮肤科系，加州大学圣地亚哥分校）；University of Chicago（芝加哥大学）；Elsevier（艾尔斯特出版社）
CORE: A Retrieve-then-Edit Framework for Counterfactual Data Generation
Indian Institute of Technology, Madras（印度理工学院马德拉斯分校）；Paul G. Allen School of Computer Science & Engineering, University of Washington（华盛顿大学保罗·G·艾伦计算机科学与工程学院）；Allen Institute of Artificial Intelligence（艾伦人工智能研究所）；Meta AI
CREST: A Joint Framework for Rationalization and Counterfactual Text Generation
Instituto de Telecomunicações（电信研究所）；Instituto Superior Técnico（里斯本高等理工学院）；LUMLIS (Lisbon ELLIS Unit)（LUMLIS（里斯本ELLIS单元））；Massachusetts Institute of Technology（麻省理工学院）；Unbabel（Unbabel公司）
