具有大量偏见的数据集当前威胁要培训有关NLU任务的值得信赖的模型。尽管取得了巨大进展，但当前的偏见方法却过分依赖偏见属性的知识。但是，属性的​​定义是难以捉摸的，并且在不同的数据集上有所不同。此外，利用输入级别的这些属性到偏置缓解可能会留下内在属性与基本决策规则之间的差距。为了缩小这一差距并解放有关偏见的监督，我们建议将缓解偏见扩展到特征空间。因此，开发了一个新型模型，即恢复具有无知识（风险）的预期功能子空间。假设由各种偏见引起的快捷键特征是为了预测而无意的，则风险将其视为冗余特征。当研究较低的歧管以去除冗余时，风险表明，具有预期功能的极低维度子空间可以牢固地表示高度偏见的数据集。经验结果表明，我们的模型可以始终如一地提高模型的概括到分布式集合，并实现新的最新性能。

大型语言模型（LLM）已在一系列自然语言理解任务上实现了最先进的表现。但是，这些LLM可能依靠数据集偏差和文物作为预测的快捷方式。这极大地损害了他们的分布（OOD）概括和对抗性鲁棒性。在本文中，我们对最新发展的综述，这些发展解决了LLMS的鲁棒性挑战。我们首先介绍LLM的概念和鲁棒性挑战。然后，我们介绍了在LLM中识别快捷方式学习行为的方法，表征了快捷方式学习的原因以及引入缓解解决方案。最后，我们确定了关键挑战，并将这一研究线的联系引入其他方向。

自然语言理解（NLU）模型倾向于依靠虚假的相关性（即数据集偏见）来在分布数据集上实现高性能，但在分布外部的数据集中的性能差。大多数现有的偏见方法通常都以偏见的特征（即引起这种虚假相关性的表面特征）来识别和削弱这些样品。但是，下降加权这些样品阻碍了从这些样品的无偏见部分学习的模型。为了应对这一挑战，在本文中，我们建议从特征空间的角度以细粒度的方式消除虚假的相关性。具体而言，我们引入了随机傅立叶特征和加权重采样，以将功能之间的依赖关系解释以减轻虚假相关性。在获得非相关的功能后，我们进一步设计了一种基于相互信息的方法来净化它们，这迫使模型学习与任务更相关的功能。对两个经过良好研究的NLU任务进行的广泛实验表明，我们的方法优于其他比较方法。

由于NLP模型实现了基准测试的最先进的性能并获得了广泛的应用程序，因此确保在现实世界中的安全部署这些模型的安全部署，例如，确保模型对未经调用或具有挑战性的情景稳健。尽管具有越来越多的学习主题，但它在视觉和NLP等应用中分别探讨了，具有多种研究中的各种定义，评估和缓解策略。在本文中，我们的目标是提供对如何定义，测量和提高NLP鲁棒性的统一调查。我们首先连接多种稳健性的定义，然后统一各种各样的工作方面识别稳健性失败和评估模型的鲁棒性。相应地，我们呈现了数据驱动，模型驱动和基于归纳的缓解策略，具有如何有效地改善NLP模型中的鲁棒性的更系统的观点。最后，我们通过概述开放的挑战和未来方向来促进在这一领域的进一步研究。

神经网络通常使预测依赖于数据集的虚假相关性，而不是感兴趣的任务的内在特性，面对分布外（OOD）测试数据的急剧下降。现有的De-Bias学习框架尝试通过偏置注释捕获特定的DataSet偏差，它们无法处理复杂的“ood方案”。其他人在低能力偏置模型或损失上隐含地识别数据集偏置，但在训练和测试数据来自相同分布时，它们会降低。在本文中，我们提出了一般的贪婪去偏见学习框架（GGD），它贪婪地训练偏置模型和基础模型，如功能空间中的梯度下降。它鼓励基础模型专注于用偏置模型难以解决的示例，从而仍然在测试阶段中的杂散相关性稳健。 GGD在很大程度上提高了各种任务的模型的泛化能力，但有时会过度估计偏置水平并降低在分配测试。我们进一步重新分析了GGD的集合过程，并将课程正规化为由课程学习启发的GGD，这取得了良好的分配和分发性能之间的权衡。对图像分类的广泛实验，对抗问题应答和视觉问题应答展示了我们方法的有效性。 GGD可以在特定于特定于任务的偏置模型的设置下学习更强大的基础模型，其中具有现有知识和自组合偏置模型而无需先验知识。

通过利用仅偏置模型的输出来调整学习目标，可以有效地显示了基于组合的脱叠方法。在本文中，我们专注于这些基于集合的方法的偏见模型，这起到了重要作用，但在现有文献中没有大量关注。从理论上讲，我们证明了脱结性能可能因偏见模型的不准确性估计而受损。凭经验，我们表明现有的偏见模型在产生准确的不确定性估计方面不足。这些发现的动机，我们建议在唯一的模型上进行校准，从而实现基于三阶段的脱叠框架，包括偏置建模，模型校准和脱叠。 NLI的实验结果和事实验证任务表明，我们提出的三阶段脱叠框架始终如一地优于传统的两级，以分配的准确性。

It has been shown that NLI models are usually biased with respect to the word-overlap between premise and hypothesis; they take this feature as a primary cue for predicting the entailment label. In this paper, we focus on an overlooked aspect of the overlap bias in NLI models: the reverse word-overlap bias. Our experimental results demonstrate that current NLI models are highly biased towards the non-entailment label on instances with low overlap, and the existing debiasing methods, which are reportedly successful on existing challenge datasets, are generally ineffective in addressing this category of bias. We investigate the reasons for the emergence of the overlap bias and the role of minority examples in its mitigation. For the former, we find that the word-overlap bias does not stem from pre-training, and for the latter, we observe that in contrast to the accepted assumption, eliminating minority examples does not affect the generalizability of debiasing methods with respect to the overlap bias.

最近的自然语言处理（NLP）技术在基准数据集中实现了高性能，主要原因是由于深度学习性能的显着改善。研究界的进步导致了最先进的NLP任务的生产系统的巨大增强，例如虚拟助理，语音识别和情感分析。然而，随着对抗性攻击测试时，这种NLP系统仍然仍然失败。初始缺乏稳健性暴露于当前模型的语言理解能力中的令人不安的差距，当NLP系统部署在现实生活中时，会产生问题。在本文中，我们通过以各种维度的系统方式概述文献来展示了NLP稳健性研究的结构化概述。然后，我们深入了解稳健性的各种维度，跨技术，指标，嵌入和基准。最后，我们认为，鲁棒性应该是多维的，提供对当前研究的见解，确定文学中的差距，以建议值得追求这些差距的方向。

最近证明，接受SGD训练的神经网络优先依赖线性预测的特征，并且可以忽略复杂的，同样可预测的功能。这种简单性偏见可以解释他们缺乏分布（OOD）的鲁棒性。学习任务越复杂，统计工件（即选择偏见，虚假相关性）的可能性就越大比学习的机制更简单。我们证明可以减轻简单性偏差并改善了OOD的概括。我们使用对其输入梯度对齐的惩罚来训练一组类似的模型以不同的方式拟合数据。我们从理论和经验上展示了这会导致学习更复杂的预测模式的学习。 OOD的概括从根本上需要超出I.I.D.示例，例如多个培训环境，反事实示例或其他侧面信息。我们的方法表明，我们可以将此要求推迟到独立的模型选择阶段。我们获得了SOTA的结果，可以在视觉域偏置数据和概括方面进行视觉识别。该方法 - 第一个逃避简单性偏见的方法 - 突出了需要更好地理解和控制深度学习中的归纳偏见。

Existing natural language understanding (NLU) models often rely on dataset biases rather than intended task-relevant features to achieve high performance on specific datasets. As a result, these models perform poorly on datasets outside the training distribution. Some recent studies address the above issue by reducing the weights of biased samples during the training process. However, these methods still encode biased latent features in representations and neglect the dynamic nature of bias, which hinders model prediction. We propose an NLU debiasing method, named debiasing contrastive learning (DCT), to simultaneously alleviate the above problems based on contrastive learning. We devise a debiasing positive sampling strategy to mitigate biased latent features by selecting the least similar biased positive samples. We also propose a dynamic negative sampling strategy to capture the dynamic influence of biases by employing a bias-only model to dynamically select the most similar biased negative samples. We conduct experiments on three NLU benchmark datasets. Experimental results show that DCT outperforms state-of-the-art baselines on out-of-distribution datasets while maintaining in-distribution performance. We also verify that DCT can reduce biased latent features from the model's representations.

Graph machine learning has been extensively studied in both academia and industry. Although booming with a vast number of emerging methods and techniques, most of the literature is built on the in-distribution hypothesis, i.e., testing and training graph data are identically distributed. However, this in-distribution hypothesis can hardly be satisfied in many real-world graph scenarios where the model performance substantially degrades when there exist distribution shifts between testing and training graph data. To solve this critical problem, out-of-distribution (OOD) generalization on graphs, which goes beyond the in-distribution hypothesis, has made great progress and attracted ever-increasing attention from the research community. In this paper, we comprehensively survey OOD generalization on graphs and present a detailed review of recent advances in this area. First, we provide a formal problem definition of OOD generalization on graphs. Second, we categorize existing methods into three classes from conceptually different perspectives, i.e., data, model, and learning strategy, based on their positions in the graph machine learning pipeline, followed by detailed discussions for each category. We also review the theories related to OOD generalization on graphs and introduce the commonly used graph datasets for thorough evaluations. Finally, we share our insights on future research directions. This paper is the first systematic and comprehensive review of OOD generalization on graphs, to the best of our knowledge.

众所周知，端到端的神经NLP体系结构很难理解，这引起了近年来为解释性建模的许多努力。模型解释的基本原则是忠诚，即，解释应准确地代表模型预测背后的推理过程。这项调查首先讨论了忠诚的定义和评估及其对解释性的意义。然后，我们通过将方法分为五类来介绍忠实解释的最新进展：相似性方法，模型内部结构的分析，基于反向传播的方法，反事实干预和自我解释模型。每个类别将通过其代表性研究，优势和缺点来说明。最后，我们从它们的共同美德和局限性方面讨论了上述所有方法，并反思未来的工作方向忠实的解释性。对于有兴趣研究可解释性的研究人员，这项调查将为该领域提供可访问且全面的概述，为进一步探索提供基础。对于希望更好地了解自己的模型的用户，该调查将是一项介绍性手册，帮助选择最合适的解释方法。

Models trained via empirical risk minimization (ERM) are known to rely on spurious correlations between labels and task-independent input features, resulting in poor generalization to distributional shifts. Group distributionally robust optimization (G-DRO) can alleviate this problem by minimizing the worst-case loss over a set of pre-defined groups over training data. G-DRO successfully improves performance of the worst-group, where the correlation does not hold. However, G-DRO assumes that the spurious correlations and associated worst groups are known in advance, making it challenging to apply it to new tasks with potentially multiple unknown spurious correlations. We propose AGRO -- Adversarial Group discovery for Distributionally Robust Optimization -- an end-to-end approach that jointly identifies error-prone groups and improves accuracy on them. AGRO equips G-DRO with an adversarial slicing model to find a group assignment for training examples which maximizes worst-case loss over the discovered groups. On the WILDS benchmark, AGRO results in 8% higher model performance on average on known worst-groups, compared to prior group discovery approaches used with G-DRO. AGRO also improves out-of-distribution performance on SST2, QQP, and MS-COCO -- datasets where potential spurious correlations are as yet uncharacterized. Human evaluation of ARGO groups shows that they contain well-defined, yet previously unstudied spurious correlations that lead to model errors.

快捷方式学习的问题在NLP中广为人知，并且近年来一直是重要的研究重点。数据中的意外相关性使模型能够轻松地求解旨在表现出高级语言理解和推理能力的任务。在本调查论文中，我们关注机器阅读理解的领域（MRC），这是展示高级语言理解的重要任务，这也遭受了一系列快捷方式。我们总结了用于测量和减轻快捷方式的可用技术，并以捷径研究进一步进展的建议结论。最重要的是，我们强调了MRC中缓解快捷方式的两个主要问题：缺乏公共挑战集，有效和可重复使用的评估的必要组成部分以及在其他领域中缺乏某些缓解技术。

Despite being responsible for state-of-the-art results in several computer vision and natural language processing tasks, neural networks have faced harsh criticism due to some of their current shortcomings. One of them is that neural networks are correlation machines prone to model biases within the data instead of focusing on actual useful causal relationships. This problem is particularly serious in application domains affected by aspects such as race, gender, and age. To prevent models from incurring on unfair decision-making, the AI community has concentrated efforts in correcting algorithmic biases, giving rise to the research area now widely known as fairness in AI. In this survey paper, we provide an in-depth overview of the main debiasing methods for fairness-aware neural networks in the context of vision and language research. We propose a novel taxonomy to better organize the literature on debiasing methods for fairness, and we discuss the current challenges, trends, and important future work directions for the interested researcher and practitioner.

尽管现有的机器阅读理解模型在许多数据集上取得了迅速的进展，但它们远非强劲。在本文中，我们提出了一个面向理解的机器阅读理解模型，以解决三种鲁棒性问题，这些问题过于敏感，稳定性和泛化。具体而言，我们首先使用自然语言推理模块来帮助模型了解输入问题的准确语义含义，以解决过度敏感性和稳定性的问题。然后，在机器阅读理解模块中，我们提出了一种记忆引导的多头注意方法，该方法可以进一步很好地理解输入问题和段落的语义含义。第三，我们提出了一种多语言学习机制来解决概括问题。最后，这些模块与基于多任务学习的方法集成在一起。我们在三个旨在衡量模型稳健性的基准数据集上评估了我们的模型，包括Dureader（健壮）和两个与小队相关的数据集。广泛的实验表明，我们的模型可以很好地解决上述三种鲁棒性问题。而且，即使在某些极端和不公平的评估下，它也比所有这些数据集中所有这些数据集的最先进模型的结果要好得多。我们工作的源代码可在以下网址获得：https：//github.com/neukg/robustmrc。

Accurate uncertainty quantification is a major challenge in deep learning, as neural networks can make overconfident errors and assign high confidence predictions to out-of-distribution (OOD) inputs. The most popular approaches to estimate predictive uncertainty in deep learning are methods that combine predictions from multiple neural networks, such as Bayesian neural networks (BNNs) and deep ensembles. However their practicality in real-time, industrial-scale applications are limited due to the high memory and computational cost. Furthermore, ensembles and BNNs do not necessarily fix all the issues with the underlying member networks. In this work, we study principled approaches to improve uncertainty property of a single network, based on a single, deterministic representation. By formalizing the uncertainty quantification as a minimax learning problem, we first identify distance awareness, i.e., the model's ability to quantify the distance of a testing example from the training data, as a necessary condition for a DNN to achieve high-quality (i.e., minimax optimal) uncertainty estimation. We then propose Spectral-normalized Neural Gaussian Process (SNGP), a simple method that improves the distance-awareness ability of modern DNNs with two simple changes: (1) applying spectral normalization to hidden weights to enforce bi-Lipschitz smoothness in representations and (2) replacing the last output layer with a Gaussian process layer. On a suite of vision and language understanding benchmarks, SNGP outperforms other single-model approaches in prediction, calibration and out-of-domain detection. Furthermore, SNGP provides complementary benefits to popular techniques such as deep ensembles and data augmentation, making it a simple and scalable building block for probabilistic deep learning. Code is open-sourced at https://github.com/google/uncertainty-baselines

大规模预训练的语言模型的出现为自然语言处理的最新进展做出了巨大贡献。许多最先进的语言模型首先在大型文本语料库上进行培训，然后在下游任务上进行微调。尽管它最近获得了成功和广泛的采用，但对预训练的语言模型的微调通常会遭受过度拟合，这会导致由于模型的复杂性极高的复杂性和下游任务的有限培训样本而导致的普遍性差。为了解决这个问题，我们提出了一个新颖有效的微调框架，称为Layerwise噪声稳定性正则化（LNSR）。具体而言，我们建议注入标准的高斯噪声或势内噪声，并将微调模型的隐藏表示形式定向。我们首先提供理论分析以支持我们方法的功效。然后，我们证明了所提出的方法的优势，而不是其他最先进的算法，包括L2-SP，MixOut和Smart。尽管这些先前的作品仅验证其方法对相对简单的文本分类任务的有效性，但我们还验证了方法对问题答案任务的有效性，而目标问题更加困难，并且可以使用更多的培训示例。此外，广泛的实验结果表明，所提出的算法不仅可以提高语言模型的内域性能，而且还可以改善域外数据的域概括性能。

Machine learning models rely on various assumptions to attain high accuracy. One of the preliminary assumptions of these models is the independent and identical distribution, which suggests that the train and test data are sampled from the same distribution. However, this assumption seldom holds in the real world due to distribution shifts. As a result models that rely on this assumption exhibit poor generalization capabilities. Over the recent years, dedicated efforts have been made to improve the generalization capabilities of these models collectively known as -- \textit{domain generalization methods}. The primary idea behind these methods is to identify stable features or mechanisms that remain invariant across the different distributions. Many generalization approaches employ causal theories to describe invariance since causality and invariance are inextricably intertwined. However, current surveys deal with the causality-aware domain generalization methods on a very high-level. Furthermore, we argue that it is possible to categorize the methods based on how causality is leveraged in that method and in which part of the model pipeline is it used. To this end, we categorize the causal domain generalization methods into three categories, namely, (i) Invariance via Causal Data Augmentation methods which are applied during the data pre-processing stage, (ii) Invariance via Causal representation learning methods that are utilized during the representation learning stage, and (iii) Invariance via Transferring Causal mechanisms methods that are applied during the classification stage of the pipeline. Furthermore, this survey includes in-depth insights into benchmark datasets and code repositories for domain generalization methods. We conclude the survey with insights and discussions on future directions.

数据增强是通过转换为机器学习的人工创建数据的人工创建，是一个跨机器学习学科的研究领域。尽管它对于增加模型的概括功能很有用，但它还可以解决许多其他挑战和问题，从克服有限的培训数据到正规化目标到限制用于保护隐私的数据的数量。基于对数据扩展的目标和应用的精确描述以及现有作品的分类法，该调查涉及用于文本分类的数据增强方法，并旨在为研究人员和从业者提供简洁而全面的概述。我们将100多种方法划分为12种不同的分组，并提供最先进的参考文献来阐述哪种方法可以通过将它们相互关联，从而阐述了哪种方法。最后，提供可能构成未来工作的基础的研究观点。