In this paper, we propose a new Deep Neural Network (DNN) testing algorithm
called the Constrained Gradient Descent (CGD) method, and an implementation we
call CGDTest aimed at exposing security and robustness issues such as
adversarial robustness and bias in DNNs. Our CGD algorithm is a
gradient-descent (GD) method, with the twist that the user can also specify
logical properties that characterize the kinds of inputs that the user may
want. This functionality sets CGDTest apart from other similar DNN testing
tools since it allows users to specify logical constraints to test DNNs not
only for $\ell_p$ ball-based adversarial robustness but, more importantly,
includes richer properties such as disguised and flow adversarial constraints,
as well as adversarial robustness in the NLP domain. We showcase the utility
and power of CGDTest via extensive experimentation in the context of vision and
NLP domains, comparing against 32 state-of-the-art methods over these diverse
domains. Our results indicate that CGDTest outperforms state-of-the-art testing
tools for $\ell_p$ ball-based adversarial robustness, and is significantly
superior in testing for other adversarial robustness, with improvements in PAR2
scores of over 1500% in some cases over the next best tool. Our evaluation
shows that our CGD method outperforms competing methods we compared against in
terms of expressibility (i.e., a rich constraint language and concomitant tool
support to express a wide variety of properties), scalability (i.e., can be
applied to very large real-world models with up to 138 million parameters), and
generality (i.e., can be used to test a plethora of model architectures).

该研究提出了一种名为 Constrained Gradient Descent（CGD）方法的新的深度神经网络（DNN）测试算法，并开发了实现称为 CGDTest，旨在揭示 DNN 中的安全性和健壮性问题，如对立攻击健壮性和偏差。该方法不仅可以为测试 DNN 的 Lp ball-based 对抗健壮性指定逻辑约束，还可以包括假面和流对抗约束以及在 NLP 领域的对抗性鲁棒性。在视觉和 NLP 领域进行广泛实验，结果表明该方法在对抗健壮性测试方面优于现有最先进的测试工具，PAR2 分数的改进在某些情况下超过 1500％，并且在表达能力、可扩展性和通用性方面均具有优势。

CGDTest: 一种用于测试神经网络的约束梯度下降算法

CGDTest: A Constrained Gradient Descent Algorithm for Testing Neural  Networks

Deep Neural Networks (DNNs) have been shown to be susceptible to Trojan
attacks. Neural Trojan is a type of targeted poisoning attack that embeds the
backdoor into the victim and is activated by the trigger in the input space.
The increasing deployment of DNNs in critical systems and the surge of
outsourcing DNN training (which makes Trojan attack easier) makes the detection
of Trojan attacks necessary. While Neural Trojan detection has been studied in
the image domain, there is a lack of solutions in the NLP domain. In this
paper, we propose a model-level Trojan detection framework by analyzing the
deviation of the model output when we introduce a specially crafted
perturbation to the input. Particularly, we extract the model's responses to
perturbed inputs as the `signature' of the model and train a meta-classifier to
determine if a model is Trojaned based on its signature. We demonstrate the
effectiveness of our proposed method on both a dataset of NLP models we create
and a public dataset of Trojaned NLP models from TrojAI. Furthermore, we
propose a lightweight variant of our detection method that reduces the
detection time while preserving the detection rates.

该论文提出了一种基于模型输出偏差分析的模型级神经木马检测框架来检测 NLP 领域的神经木马，并提出了一种轻量级变体以提高检测速度。

基于扰动敏感性的自然语言处理神经木马检测框架 PerD

PerD: Perturbation Sensitivity-based Neural Trojan Detection Framework on NLP Applications

Distant and weak supervision allow to obtain large amounts of labeled
training data quickly and cheaply, but these automatic annotations tend to
contain a high amount of errors. A popular technique to overcome the negative
effects of these noisy labels is noise modelling where the underlying noise
process is modelled. In this work, we study the quality of these estimated
noise models from the theoretical side by deriving the expected error of the
noise model. Apart from evaluating the theoretical results on commonly used
synthetic noise, we also publish NoisyNER, a new noisy label dataset from the
NLP domain that was obtained through a realistic distant supervision technique.
It provides seven sets of labels with differing noise patterns to evaluate
different noise levels on the same instances. Parallel, clean labels are
available making it possible to study scenarios where a small amount of
gold-standard data can be leveraged. Our theoretical results and the
corresponding experiments give insights into the factors that influence the
noise model estimation like the noise distribution and the sampling technique.

本文通过研究嘈杂标注的误差建模，提出了一种理论上的噪声模型误差估计方法，并提供了用于评估不同噪声级别的 NoisyNER 数据集，这一研究旨在探索影响噪声模型估计的因素，包括噪声分布和抽样技术。

分析逼真嘈杂标签数据的噪声模型误差

Analysing the Noise Model Error for Realistic Noisy Label Data

Few-shot natural language processing (NLP) refers to NLP tasks that are
accompanied with merely a handful of labeled examples. This is a real-world
challenge that an AI system must learn to handle. Usually we rely on collecting
more auxiliary information or developing a more efficient learning algorithm.
However, the general gradient-based optimization in high capacity models, if
training from scratch, requires many parameter-updating steps over a large
number of labeled examples to perform well (Snell et al., 2017). If the target
task itself cannot provide more information, how about collecting more tasks
equipped with rich annotations to help the model learning? The goal of
meta-learning is to train a model on a variety of tasks with rich annotations,
such that it can solve a new task using only a few labeled samples. The key
idea is to train the model's initial parameters such that the model has maximal
performance on a new task after the parameters have been updated through zero
or a couple of gradient steps. There are already some surveys for
meta-learning, such as (Vilalta and Drissi, 2002; Vanschoren, 2018; Hospedales
et al., 2020). Nevertheless, this paper focuses on NLP domain, especially
few-shot applications. We try to provide clearer definitions, progress summary
and some common datasets of applying meta-learning to few-shot NLP.

本文旨在为小样本自然语言处理元学习领域提供更清晰的定义、进展总结和一些常见的数据集，通过在具有丰富注释的各种任务上训练模型，实现在只有少量标记示例的情况下解决新任务的目标。