Task-oriented dialogue systems often assist users with personal or confidential matters. For this reason, the developers of such a system are generally prohibited from observing actual usage. So how can they know where the system is failing and needs more training data or new functionality? In this work, we study ways in which realistic user utterances can be generated synthetically, to help increase the linguistic and functional coverage of the system, without compromising the privacy of actual users. To this end, we propose a two-stage Differentially Private (DP) generation method which first generates latent semantic parses, and then generates utterances based on the parses. Our proposed approach improves MAUVE by 3.8$\times$ and parse tree node-type overlap by 1.4$\times$ relative to current approaches for private synthetic data generation, improving both on fluency and semantic coverage. We further validate our approach on a realistic domain adaptation task of adding new functionality from private user data to a semantic parser, and show gains of 1.3$\times$ on its accuracy with the new feature.

差异化（DP）学习在建立大型文本模型方面的成功有限，并尝试直接将差异化私有随机梯度下降（DP-SGD）应用于NLP任务，从而导致了大量的性能下降和高度计算的开销。我们表明，通过（1）使用大型验证模型可以缓解这种性能下降； （2）适合DP优化的超参数； （3）与训练过程对齐的微调目标。通过正确设定这些因素，我们将获得私人NLP模型，以优于最先进的私人培训方法和强大的非私人基准 - 通过直接对中等大小的Corpora进行DP优化的预审计模型。为了解决使用大型变压器运行DP-SGD的计算挑战，我们提出了一种存储器保存技术，该技术允许DP-SGD中的剪辑在不实例化模型中任何层的每个示例梯度的情况下运行。该技术使私人训练变压器的内存成本几乎与非私人培训相同，并以适度的运行时间开销。与传统的观点相反，即DP优化在学习高维模型（由于尺寸缩放的噪声）方面失败的经验结果表明，使用预审预周化模型的私人学习往往不会遭受维度依赖性性能降低的障碍。

我们探索使用大型预用语言模型作为少量语义解析器。语义解析中的目标是给定自然语言输入的结构化含义表示。但是，培训语言模型以生成自然语言。为了弥合差距，我们使用语言模型来解释进入一个类似于英语的受控的子宫内的输入，可以自动映射到目标含义表示表示。我们的结果表明，只有少量的数据和较少的代码转换为类似英语的代表，我们为快速启动语义解析器的蓝图导致了对多个社区任务的令人惊讶的有效性能，大大超过基线方法也在相同的限制上培训数据。

Pre-training large transformer models with in-domain data improves domain adaptation and helps gain performance on the domain-specific downstream tasks. However, sharing models pre-trained on potentially sensitive data is prone to adversarial privacy attacks. In this paper, we asked to which extent we can guarantee privacy of pre-training data and, at the same time, achieve better downstream performance on legal tasks without the need of additional labeled data. We extensively experiment with scalable self-supervised learning of transformer models under the formal paradigm of differential privacy and show that under specific training configurations we can improve downstream performance without sacrifying privacy protection for the in-domain data. Our main contribution is utilizing differential privacy for large-scale pre-training of transformer language models in the legal NLP domain, which, to the best of our knowledge, has not been addressed before.

差异隐私（DP）提供了正式的隐私保证，以防止对手可以访问机器学习模型，从而从提取有关单个培训点的信息。最受欢迎的DP训练方法是差异私有随机梯度下降（DP-SGD），它通过在训练过程中注入噪声来实现这种保护。然而，以前的工作发现，DP-SGD通常会导致标准图像分类基准的性能显着降解。此外，一些作者假设DP-SGD在大型模型上固有地表现不佳，因为保留隐私所需的噪声规范与模型维度成正比。相反，我们证明了过度参数化模型上的DP-SGD可以比以前想象的要好得多。将仔细的超参数调整与简单技术结合起来，以确保信号传播并提高收敛速率，我们获得了新的SOTA，而没有额外数据的CIFAR-10，在81.4％的81.4％下（8，10^{ - 5}） - 使用40 -layer wide-Resnet，比以前的SOTA提高了71.7％。当对预训练的NFNET-F3进行微调时，我们在ImageNet（0.5，8*10^{ - 7}）下达到了83.8％的TOP-1精度。此外，我们还在（8，8 \ cdot 10^{ - 7}）下达到了86.7％的TOP-1精度，DP仅比当前的非私人SOTA仅4.3％。我们认为，我们的结果是缩小私人图像分类和非私有图像分类之间准确性差距的重要一步。

最近的数据提取攻击暴露了语言模型可以记住一些培训样本逐字。这是一种漏洞，可以损害模型培训数据的隐私。在这项工作中，我们介绍了子句：私人私人下一象征预测的实用协议，旨在防止在公共语料库预训练后在私人语料库中进行微调的语言模型的隐私违规。我们展示子子句通过放松差异私密预测，限制了私人语料库中的任何单独用户所唯一的信息的泄漏。重要的是，子提M允许一个紧张，数据相关的隐私会计机制，它允许它挫败现有的数据提取攻击，同时保持语言模型的效用。子句是即使在公开释放由大型变压器的模型等基于GPT-2的基于大型变换器的模型制作的数千个下一令牌预测，也是第一个维护隐私的协议。

Differentially private deep learning has recently witnessed advances in computational efficiency and privacy-utility trade-off. We explore whether further improvements along the two axes are possible and provide affirmative answers leveraging two instantiations of \emph{group-wise clipping}. To reduce the compute time overhead of private learning, we show that \emph{per-layer clipping}, where the gradient of each neural network layer is clipped separately, allows clipping to be performed in conjunction with backpropagation in differentially private optimization. This results in private learning that is as memory-efficient and almost as fast per training update as non-private learning for many workflows of interest. While per-layer clipping with constant thresholds tends to underperform standard flat clipping, per-layer clipping with adaptive thresholds matches or outperforms flat clipping under given training epoch constraints, hence attaining similar or better task performance within less wall time. To explore the limits of scaling (pretrained) models in differentially private deep learning, we privately fine-tune the 175 billion-parameter GPT-3. We bypass scaling challenges associated with clipping gradients that are distributed across multiple devices with \emph{per-device clipping} that clips the gradient of each model piece separately on its host device. Privately fine-tuning GPT-3 with per-device clipping achieves a task performance at $\epsilon=1$ better than what is attainable by non-privately fine-tuning the largest GPT-2 on a summarization task.

We demonstrate that it is possible to train large recurrent language models with user-level differential privacy guarantees with only a negligible cost in predictive accuracy. Our work builds on recent advances in the training of deep networks on user-partitioned data and privacy accounting for stochastic gradient descent. In particular, we add user-level privacy protection to the federated averaging algorithm, which makes "large step" updates from user-level data. Our work demonstrates that given a dataset with a sufficiently large number of users (a requirement easily met by even small internet-scale datasets), achieving differential privacy comes at the cost of increased computation, rather than in decreased utility as in most prior work. We find that our private LSTM language models are quantitatively and qualitatively similar to un-noised models when trained on a large dataset.

本文介绍了Okapi，用于自然语言的新数据集到可执行的Web应用程序编程接口（NL2API）。此数据集是英文，包含22,508个问题和9,019个独特的API呼叫，涵盖三个域。我们为NL2API定义了新的组成泛化任务，该任务探讨了在推理阶段中的培训中从简单API调用外推开的模型能力。此外，该模型必须生成正确执行的API调用，而不是与现有方法进行正确执行，该方法评估具有占位符值的查询。我们的数据集与大多数现有的组合语义解析数据集不同，因为它是一个非合成数据集，研究了低资源设置中的组成概括。 Okapi是创建现实数据集和基准的一步，用于研究与现有数据集和任务一起学习组成泛化。我们报告了在各种扫描和okapi数据集任务上培训的序列到序列基线模型的泛化能力。当从简单API调用概括到更复杂的API调用时，最佳模型可实现15 \％精确匹配的准确性。这突出了未来研究的一些挑战。 okapi数据集和任务在https://aka.ms/nl2api/data上公开使用。

随着语言模型的不断增加，它对于保护这些模型免于泄漏私人信息变得至关重要。以前的工作试图通过培训具有不同隐私保证的基于RNN的语言模型来应对这一挑战。但是，将经典的差异隐私应用于语言模型会导致模型性能差，因为基本隐私概念过于困惑，并且为数据中所有令牌提供了不体化的保护。鉴于自然语言中的私人信息很少（例如，电子邮件的大部分可能无法携带个人身份信息），我们提出了一个新的隐私概念，选择性差异隐私，以提供严格的数据，以保证数据的敏感部分改善模型实用程序。为了实现这样一个新的概念，我们为基于RNN的语言模型开发了相应的隐私机制，即选择性DPSGD。除了语言建模外，我们还将方法应用于更具体的应用程序 - dialog系统。语言建模和对话系统建设的实验表明，与基线相比，在各种隐私攻击下，提议的保留隐私机制可以实现更好的公用事业，同时保持安全。数据和代码在https://github.com/wyshi/lm_privacy上发布，以促进未来的研究。

Privacy preserving deep learning is an emerging field in machine learning that aims to mitigate the privacy risks in the use of deep neural networks. One such risk is training data extraction from language models that have been trained on datasets , which contain personal and privacy sensitive information. In our study, we investigate the extent of named entity memorization in fine-tuned BERT models. We use single-label text classification as representative downstream task and employ three different fine-tuning setups in our experiments, including one with Differentially Privacy (DP). We create a large number of text samples from the fine-tuned BERT models utilizing a custom sequential sampling strategy with two prompting strategies. We search in these samples for named entities and check if they are also present in the fine-tuning datasets. We experiment with two benchmark datasets in the domains of emails and blogs. We show that the application of DP has a huge effect on the text generation capabilities of BERT. Furthermore, we show that a fine-tuned BERT does not generate more named entities entities specific to the fine-tuning dataset than a BERT model that is pre-trained only. This suggests that BERT is unlikely to emit personal or privacy sensitive named entities. Overall, our results are important to understand to what extent BERT-based services are prone to training data extraction attacks.

This paper describes a testing methodology for quantitatively assessing the risk that rare or unique training-data sequences are unintentionally memorized by generative sequence models-a common type of machine-learning model. Because such models are sometimes trained on sensitive data (e.g., the text of users' private messages), this methodology can benefit privacy by allowing deep-learning practitioners to select means of training that minimize such memorization.In experiments, we show that unintended memorization is a persistent, hard-to-avoid issue that can have serious consequences. Specifically, for models trained without consideration of memorization, we describe new, efficient procedures that can extract unique, secret sequences, such as credit card numbers. We show that our testing strategy is a practical and easy-to-use first line of defense, e.g., by describing its application to quantitatively limit data exposure in Google's Smart Compose, a commercial text-completion neural network trained on millions of users' email messages.

我们考虑使用迷你批量梯度进行差异隐私（DP）的培训模型。现有的最先进的差异私有随机梯度下降（DP-SGD）需要通过采样或洗机来获得最佳隐私/准确性/计算权衡的隐私放大。不幸的是，在重要的实际情况下，精确采样和洗牌的精确要求可能很难获得，特别是联邦学习（FL）。我们设计和分析跟随 - 正规的领导者（DP-FTRL）的DP变体，其比较（理论上和经验地）与放大的DP-SGD相比，同时允许更灵活的数据访问模式。DP-FTRL不使用任何形式的隐私放大。该代码可在https://github.com/google-Research/federated/tree/master/dp_ftrl和https://github.com/google-reesearch/dp-ftrl处获得。

深度神经网络（DNNS）铰接对大型数据集的可用性的最新成功;但是，对此类数据集的培训经常为敏感培训信息构成隐私风险。在本文中，我们的目标是探讨生成模型和梯度稀疏性的力量，并提出了一种可扩展的隐私保留生成模型数据标准。与标准展示隐私保留框架相比，允许教师对一维预测进行投票，在高维梯度向量上投票在隐私保存方面具有挑战性。随着需要尺寸减少技术，我们需要在（1）之间的改进之间导航精致的权衡空间，并进行SGD收敛的放缓。为了解决这一点，我们利用通信高效学习，并通过将顶-K压缩与相应的噪声注入机构相结合，提出一种新的噪声压缩和聚集方法TopAGG。理论上，我们证明了DataLens框架保证了其生成数据的差异隐私，并提供了其收敛性的分析。为了展示DataLens的实际使用情况，我们对不同数据集进行广泛的实验，包括Mnist，Fashion-Mnist和高维Celeba，并且我们表明，DataLens显着优于其他基线DP生成模型。此外，我们改进了所提出的Topagg方法，该方法是DP SGD培训的主要构建块之一，并表明它能够在大多数情况下实现比最先进的DP SGD方法更高的效用案件。我们的代码在HTTPS://github.com/ai-secure/datalens公开提供。

在本文中，我们研究了具有差异隐私（DP）的学习图神经网络（GNN）的问题。我们提出了一种基于聚合扰动（GAP）的新型差异私有GNN，该GNN为GNN的聚合函数添加了随机噪声，以使单个边缘（边缘级隐私）或单个节点的存在统计上的存在及其所有邻接边缘（ - 级别的隐私）。 GAP的新体系结构是根据私人学习的细节量身定制的，由三个单独的模块组成：（i）编码器模块，我们在不依赖边缘信息的情况下学习私人节点嵌入； （ii）聚合模块，其中我们根据图结构计算嘈杂的聚合节点嵌入； （iii）分类模块，我们在私有聚合上训练神经网络进行节点分类，而无需进一步查询图表。 GAP比以前的方法的主要优势在于，它可以从多跳社区的聚合中受益，并保证边缘级别和节点级别的DP不仅用于培训，而且可以推断出培训的隐私预算以外的额外费用。我们使用R \'Enyi DP来分析GAP的正式隐私保证，并在三个真实世界图数据集上进行经验实验。我们证明，与最先进的DP-GNN方法和天真的MLP基线相比，GAP提供了明显更好的准确性私人权衡权衡。

Natural Language Generation (NLG) has improved exponentially in recent years thanks to the development of sequence-to-sequence deep learning technologies such as Transformer-based language models. This advancement has led to more fluent and coherent NLG, leading to improved development in downstream tasks such as abstractive summarization, dialogue generation and data-to-text generation. However, it is also apparent that deep learning based generation is prone to hallucinate unintended text, which degrades the system performance and fails to meet user expectations in many real-world scenarios. To address this issue, many studies have been presented in measuring and mitigating hallucinated texts, but these have never been reviewed in a comprehensive manner before. In this survey, we thus provide a broad overview of the research progress and challenges in the hallucination problem in NLG. The survey is organized into two parts: (1) a general overview of metrics, mitigation methods, and future directions; and (2) an overview of task-specific research progress on hallucinations in the following downstream tasks, namely abstractive summarization, dialogue generation, generative question answering, data-to-text generation, machine translation, and visual-language generation. This survey serves to facilitate collaborative efforts among researchers in tackling the challenge of hallucinated texts in NLG.

大型语言模型被显示为记住隐私信息，例如培训数据中的社会保险号。鉴于培训语料库的巨大规模，筛选和自动筛选和过滤这些隐私数据是一项挑战。在本文中，我们提出了秘密编辑的培训（CRT），这是一种培训语言生成模型的方法，同时保护机密细分市场。我们从差异隐私（解决一个相关但独特的问题）中借鉴了想法，并表明我们的方法能够通过随机将培训过程的部分随机化来防止意外的记忆。此外，我们证明了通过近似正确的筛选策略进行修复会放大机密性保证。我们实施LSTM和GPT语言模型的方法。我们的实验结果表明，通过CRT训练的模型获得了几乎相同的困惑，同时保持了强大的机密性。

A major direction in differentially private machine learning is differentially private fine-tuning: pretraining a model on a source of "public data" and transferring the extracted features to downstream tasks. This is an important setting because many industry deployments fine-tune publicly available feature extractors on proprietary data for downstream tasks. In this paper, we use features extracted from state-of-the-art open source models to solve benchmark tasks in computer vision and natural language processing using differentially private fine-tuning. Our key insight is that by accelerating training, we can quickly drive the model parameters to regions in parameter space where the impact of noise is minimized. In doing so, we recover the same performance as non-private fine-tuning for realistic values of epsilon in [0.01, 1.0] on benchmark image classification datasets including CIFAR100.

Privacy noise may negate the benefits of using adaptive optimizers in differentially private model training. Prior works typically address this issue by using auxiliary information (e.g., public data) to boost the effectiveness of adaptive optimization. In this work, we explore techniques to estimate and efficiently adapt to gradient geometry in private adaptive optimization without auxiliary data. Motivated by the observation that adaptive methods can tolerate stale preconditioners, we propose differentially private adaptive training with delayed preconditioners (DP^2), a simple method that constructs delayed but less noisy preconditioners to better realize the benefits of adaptivity. Theoretically, we provide convergence guarantees for our method for both convex and non-convex problems, and analyze trade-offs between delay and privacy noise reduction. Empirically, we explore DP^2 across several real-world datasets, demonstrating that it can improve convergence speed by as much as 4x relative to non-adaptive baselines and match the performance of state-of-the-art optimization methods that require auxiliary data.

具有差异隐私（DP）的文本重写提供了具体的理论保证，可以保护个人在文本文档中的隐私。实际上，现有系统可能缺乏验证其隐私索赔的手段，从而导致透明度和可重复性问题。我们介绍了DP-Rewrite，这是一个开源框架，用于差异化文本重写，旨在通过模块化，可扩展和高度定制来解决这些问题。我们的系统结合了各种下游数据集，模型，培训前程序和评估指标，以提供一种灵活的方式来领导和验证私人文本重写研究。为了在实践中展示我们的软件，我们提供了一组实验，作为对熟练DP文本重写系统的案例研究，检测其预训练方法中的隐私泄漏。我们的系统公开可用，我们希望它将帮助社区使DP文本重写研究更容易访问和透明。