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
Tanay Dixit, Bhargavi Paranjape, Hannaneh Hajishirzi, Luke Zettlemoyer
Counterfactual data augmentation (CDA) -- i.e., adding minimally perturbed inputs during training -- helps reduce model reliance on spurious correlations and improves generalization to out-of-distribution (OOD) data. Prior work on generating counterfactuals only considered restricted classes of perturbations, limiting their effectiveness. We present COunterfactual Generation via Retrieval and Editing (CORE), a retrieval-augmented generation framework for creating diverse counterfactual perturbations for CDA. For each training example, CORE first performs a dense retrieval over a task-related unlabeled text corpus using a learned bi-encoder and extracts relevant counterfactual excerpts. CORE then incorporates these into prompts to a large language model with few-shot learning capabilities, for counterfactual editing. Conditioning language model edits on naturally occurring data results in diverse perturbations. Experiments on natural language inference and sentiment analysis benchmarks show that CORE counterfactuals are more effective at improving generalization to OOD data compared to other DA approaches. We also show that the CORE retrieval framework can be used to encourage diversity in manually authored perturbations
Parallel Universes, Parallel Languages: A Comprehensive Study on LLM-based Multilingual Counterfactual Example Generation
Technische Universität Berlin（柏林工业大学）；German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心（DFKI））；University of Marburg（马尔堡大学）；LMU Munich（慕尼黑大学）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心（MCML））；BIFOLD – Berlin Institute for the Foundations of Learning and Data（柏林学习与数据基础研究所（BIFOLD））
People Make Better Edits: Measuring the Efficacy of LLM-Generated Counterfactually Augmented Data for Harmful Language Detection
RWTH Aachen University（亚琛工业大学）；University of Konstanz（康斯坦茨大学）；GESIS - Leibniz Institute for Social Sciences（GESIS-莱布尼茨社会科学研究所）；Sapienza University of Rome（罗马智慧大学）；University of Copenhagen（哥本哈根大学）
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）
Measuring Social Bias in Vision-Language Models with Face-Only Counterfactuals from Real Photos
School of Intelligence Science and Engineering, Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳）智能科学与工程学院）；School of Information Science and Engineering, Ningbo University（宁波大学信息科学与工程学院）；School of Information Science and Technology, University of Science and Technology of China（中国科学技术大学信息科学与技术学院）
