Understanding and Mitigating the Bias Inheritance in LLM-based Data Augmentation on Downstream Tasks
The Chinese University of Hong Kong（香港中文大学）；Carnegie Mellon University（卡内基梅隆大学）；Institute of Science Tokyo（东京科学大学）；UIUC（伊利诺伊大学厄巴纳-香槟分校）；UC Santa Barbara（加利福尼亚大学圣巴巴拉分校）；William & Mary（威廉玛丽学院）
Miaomiao Li, Hao Chen, Yang Wang, Tingyuan Zhu, Weijia Zhang, Kaijie Zhu, Kam-Fai Wong, Jindong Wang
Generating synthetic datasets via large language models (LLMs) themselves has emerged as a promising approach to improve LLM performance. However, LLMs inherently reflect biases present in their training data, leading to a critical challenge: when these models generate synthetic data for training, they may propagate and amplify their inherent biases that can significantly impact model fairness and robustness on downstream tasks--a phenomenon we term bias inheritance. This work presents the first systematic investigation in understanding, analyzing, and mitigating bias inheritance. We study this problem by fine-tuning LLMs with a combined dataset consisting of original and LLM-augmented data, where bias ratio represents the proportion of augmented data. Through systematic experiments across 10 classification and generation tasks, we analyze how 6 different types of biases manifest at varying bias ratios. Our results reveal that bias inheritance has nuanced effects on downstream tasks, influencing both classification tasks and generation tasks differently. Then, our analysis identifies three key misalignment factors: misalignment of values, group data, and data distributions. Based on these insights, we propose three mitigation strategies: token-based, mask-based, and loss-based approaches. Experiments demonstrate that these strategies also work differently on various tasks and bias, indicating the substantial challenges to fully mitigate bias inheritance. We hope this work can provide valuable insights to the research of LLM data augmentation.
In recent years, pretrained neural language models (PNLMs) have taken the field of natural language processing by storm, achieving new benchmarks and state-of-the-art performances. These models often rely heavily on annotated data, which may not always be available. Data scarcity are commonly found in specialized domains, such as medical, or in low-resource languages that are underexplored by AI research. In this dissertation, we focus on mitigating data scarcity using data augmentation and neural ensemble learning techniques for neural language models. In both research directions, we implemen…
BRIDGE the Gap: Mitigating Bias Amplification in Automated Scoring of English Language Learners via Inter-group Data Augmentation
School of Computing, University of Georgia, Athens, GA, USA（佐治亚大学计算机学院，美国亚特兰大，GA）；Educational Testing Service, Princeton, NJ, USA（教育测试服务，美国新泽西州普林斯顿，NJ）；AI4STEM Education Center, University of Georgia, Athens, GA, USA（AI4STEM教育中心，佐治亚大学，美国亚特兰大，GA）；The Hong Kong Polytechnic University, Hong Kong, China（香港理工大学，中国香港）
Truth-Aware Context Selection: Mitigating Hallucinations of Large Language Models Being Misled by Untruthful Contexts
Institute of Computing Technology, Chinese Academy of Sciences (ICT/CAS)（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Key Laboratory of AI Safety, Chinese Academy of Sciences（中国科学院人工智能安全重点实验室）
A Simplified Retriever to Improve Accuracy of Phenotype Normalizations by Large Language Models
Glyndŵr University（格林多大学）；National Research Council (CNR)（国家研究委员会）；Lancaster University（兰卡斯特大学）；University of Illinois at Chicago（芝加哥大学伊利诺伊分校）；Missouri State University（密苏里州立大学）
Getting Sick After Seeing a Doctor? Diagnosing and Mitigating Knowledge Conflicts in Event Temporal Reasoning
Hong Kong University of Science and Technology（香港科技大学）；University of Southern California（南加州大学）；Tencent AI Lab（腾讯AI实验室）；University of California, Davis（加州大学戴维斯分校）
Huawei Noah’s Ark Lab（华为诺亚方舟实验室）；University of Waterloo（滑铁卢大学）
Federated Learning (FL) is a prominent framework that enables training a centralized model while securing user privacy by fusing local, decentralized models. In this setting, one major obstacle is data heterogeneity, i.e., each client having non-identically and independently distributed (non-IID) data. This is analogous to the context of Domain Generalization (DG), where each client can be treated as a different domain. However, while many approaches in DG tackle data heterogeneity from the algorithmic perspective, recent evidence suggests that data augmentation can induce equal or greater per…
Mitigating Long-Tail Bias in HOI Detection via Adaptive Diversity Cache
College of Future Information Technology, Fudan University（复旦大学未来信息技术学院）；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）；Institute of Artificial Intelligence (TeleAI), China Telecom（中国电信人工智能研究院（TeleAI））
Mitigating Degree Bias in Graph Representation Learning with Learnable Structural Augmentation and Structural Self-Attention
Department of Artificial Intelligence, The Catholic University of Korea（人工智能系，韩国天主大学）；Data Assimilation Group, Korea Institute of Atmospheric Prediction Systems (KIAPS)（数据同化组，韩国大气预测研究所）
DeepSweep: An Evaluation Framework for Mitigating DNN Backdoor Attacks using Data Augmentation
Tsinghua University（清华大学）；University of California San Diego（加利福尼亚大学圣迭戈分校）；Chongqing University（重庆大学）；Nanyang Technological University（南洋理工大学）；Texas A&M University Commerce（得克萨斯A&M大学康默斯分校）；The University of Texas at Dallas（得克萨斯大学达拉斯分校）
Improving Group Fairness in Tensor Completion via Imbalance Mitigating Entity Augmentation
Department of Computer Science and Engineering, University of California, Riverside, Riverside, CA, USA（计算机科学与工程系，加州大学河滨分校）；Siebel School of Computing and Data Science, University of Illinois Urbana-Champaign, Urbana, IL, USA（计算与数据科学学院，伊利诺伊大学厄巴纳-香槟分校）
Topology Reorganized Graph Contrastive Learning with Mitigating Semantic Drift
College of Computer Science & Technology, Nanjing University of Aeronautics & Astronautics（南京航空航天大学计算机科学与技术学院）；MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, Nanjing University of Aeronautics & Astronautics（南京航空航天大学工信部模式分析与机器智能重点实验室）
Netaji Subhas University of Technology（内塔吉·苏巴斯理工大学）；Northeastern University（东北大学）
Intimacy is an essential element of human relationships and language is a crucial means of conveying it. Textual intimacy analysis can reveal social norms in different contexts and serve as a benchmark for testing computational models' ability to understand social information. In this paper, we propose a novel weak-labeling strategy for data augmentation in text regression tasks called WADER. WADER uses data augmentation to address the problems of data imbalance and data scarcity and provides a method for data augmentation in cross-lingual, zero-shot tasks. We benchmark the performance of Stat…
ClearGCD: Mitigating Shortcut Learning For Robust Generalized Category Discovery
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Loughborough University（洛桑大学）
Self-supervised learning (SSL) methods have resulted in broad improvements to neural network performance by leveraging large, untapped collections of unlabeled data to learn generalized underlying structure. In this work, we harness unsupervised data augmentation (UDA), an SSL technique, to mitigate backdoor or Trojan attacks on deep neural networks. We show that UDA is more effective at removing trojans than current state-of-the-art methods for both feature space and point triggers, over a range of model architectures, trojans, and data quantities provided for trojan removal. These results de…
Practical sequence classification tasks in natural language processing often suffer from low training data availability for target classes. Recent works towards mitigating this problem have focused on transfer learning using embeddings pre-trained on often unrelated tasks, for instance, language modeling. We adopt an alternative approach by transfer learning on an ensemble of related tasks using prototypical networks under the meta-learning paradigm. Using intent classification as a case study, we demonstrate that increasing variability in training tasks can significantly improve classificatio…
University of Oxford（牛津大学）；University of Warwick（华威大学）；Universitat Pompeu Fabra（庞培法布拉大学）；University of Kaiserslautern（凯泽斯劳滕大学）；German Research Centre for Artificial Intelligence (DFKI)（德国人工智能研究中心）
Increasing interest in privacy-preserving machine learning has led to new and evolved approaches for generating private synthetic data from undisclosed real data. However, mechanisms of privacy preservation can significantly reduce the utility of synthetic data, which in turn impacts downstream tasks such as learning predictive models or inference. We propose several re-weighting strategies using privatised likelihood ratios that not only mitigate statistical bias of downstream estimators but also have general applicability to differentially private generative models. Through large-scale empir…
Adversarial training suffers from the issue of robust overfitting, which seriously impairs its generalization performance. Data augmentation, which is effective at preventing overfitting in standard training, has been observed by many previous works to be ineffective in mitigating overfitting in adversarial training. This work proves that, contrary to previous findings, data augmentation alone can significantly boost accuracy and robustness in adversarial training. We find that the hardness and the diversity of data augmentation are important factors in combating robust overfitting. In general…
The generalization gap in reinforcement learning (RL) has been a significant obstacle that prevents the RL agent from learning general skills and adapting to varying environments. Increasing the generalization capacity of the RL systems can significantly improve their performance on real-world working environments. In this work, we propose a novel policy-aware adversarial data augmentation method to augment the standard policy learning method with automatically generated trajectory data. Different from the commonly used observation transformation based data augmentations, our proposed method a…
Model-Based Offline Reinforcement Learning with Adversarial Data Augmentation
National Key Laboratory for Novel Software Technology, Nanjing University（南京大学现代软件技术国家重点实验室）；City University of Hong Kong（香港城市大学）；School of Computer Science, Nanjing University of Posts and Telecommunications（南京邮电大学计算机学院）；Department of Computer Science, University of Liverpool（利物浦大学计算机系）
Adversarial training augments the training set with perturbations to improve the robust error (over worst-case perturbations), but it often leads to an increase in the standard error (on unperturbed test inputs). Previous explanations for this tradeoff rely on the assumption that no predictor in the hypothesis class has low standard and robust error. In this work, we precisely characterize the effect of augmentation on the standard error in linear regression when the optimal linear predictor has zero standard and robust error. In particular, we show that the standard error could increase even …
