Generating counterfactual test-cases is an important backbone for testing NLP
models and making them as robust and reliable as traditional software. In
generating the test-cases, a desired property is the ability to control the
test-case generation in a flexible manner to test for a large variety of
failure cases and to explain and repair them in a targeted manner. In this
direction, significant progress has been made in the prior works by manually
writing rules for generating controlled counterfactuals. However, this approach
requires heavy manual supervision and lacks the flexibility to easily introduce
new controls. Motivated by the impressive flexibility of the plug-and-play
approach of PPLM, we propose bringing the framework of plug-and-play to
counterfactual test case generation task. We introduce CASPer, a plug-and-play
counterfactual generation framework to generate test cases that satisfy goal
attributes on demand. Our plug-and-play model can steer the test case
generation process given any attribute model without requiring
attribute-specific training of the model. In experiments, we show that CASPer
effectively generates counterfactual text that follow the steering provided by
an attribute model while also being fluent, diverse and preserving the original
content. We also show that the generated counterfactuals from CASPer can be
used for augmenting the training data and thereby fixing and making the test
model more robust.

CASPer 框架使用插入即用的方法生成能够满足目标属性的对抗文本，用于补充训练数据，从而提高测试模型的鲁棒性和可靠性。

插拔式因果文本生成：提升模型鲁棒性

Plug and Play Counterfactual Text Generation for Model Robustness

The Anscombe transform offers an approximate conversion of a Poisson random
variable into a Gaussian one. This transform is important and appealing, as it
is easy to compute, and becomes handy in various inverse problems with Poisson
noise contamination. Solution to such problems can be done by first applying
the Anscombe transform, then applying a Gaussian-noise-oriented restoration
algorithm of choice, and finally applying an inverse Anscombe transform. The
appeal in this approach is due to the abundance of high-performance restoration
algorithms designed for white additive Gaussian noise (we will refer to these
hereafter as "Gaussian-solvers"). This process is known to work well for high
SNR images, where the Anscombe transform provides a rather accurate
approximation. When the noise level is high, the above path loses much of its
effectiveness, and the common practice is to replace it with a direct treatment
of the Poisson distribution. Naturally, with this we lose the ability to
leverage on vastly available Gaussian-solvers.
In this work we suggest a novel method for coupling Gaussian denoising
algorithms to Poisson noisy inverse problems, which is based on a general
approach termed "Plug-and-Play". Deploying the Plug-and-Play approach to such
problems leads to an iterative scheme that repeats several key steps: 1) A
convex programming task of simple form that can be easily treated; 2) A
powerful Gaussian denoising algorithm of choice; and 3) A simple update step.
Such a modular method, just like the Anscombe transform, enables other
developers to plug their own Gaussian denoising algorithms to our scheme in an
easy way. While the proposed method bares some similarity to the Anscombe
operation, it is in fact based on a different mathematical basis, which holds
true for all SNR ranges.

本文提出了一种基于 “Plug-and-Play” 方法的模块化迭代方案，将高斯去噪算法与泊松噪声反问题耦合，可以让其他开发人员轻松地将其自己的高斯去噪算法插入到我们的方案中。