Language models have demonstrated the ability to generate highly fluent text;
however, it remains unclear whether their output retains coherent high-level
structure (e.g., story progression). Here, we propose to apply a statistical
tool, model criticism in latent space, to evaluate the high-level structure of
the generated text. Model criticism compares the distributions between real and
generated data in a latent space obtained according to an assumptive generative
process. Different generative processes identify specific failure modes of the
underlying model. We perform experiments on three representative aspects of
high-level discourse -- coherence, coreference, and topicality -- and find that
transformer-based language models are able to capture topical structures but
have a harder time maintaining structural coherence or modeling coreference.

使用潜在空间中的统计工具，比较语言模型生成的文本与真实数据的分布，评估生成文本的高级结构，发现基于 Transformer 的语言模型能够捕捉主题结构，但在维持结构连贯性和建模指代关系方面存在困难。

长文本生成的模型评估

Model Criticism for Long-Form Text Generation

This paper is a note on new directions and methodologies for validation and
explanation of Machine Learning (ML) models employed for retail credit scoring
in finance. Our proposed framework draws motivation from the field of
Artificial Intelligence (AI) security and adversarial ML where the need for
certifying the performance of the ML algorithms in the face of their
overwhelming complexity poses a need for rethinking the traditional notions of
model architecture selection, sensitivity analysis and stress testing. Our
point of view is that the phenomenon of adversarial perturbations when detached
from the AI security domain, has purely algorithmic roots and fall within the
scope of model risk assessment. We propose a model criticism and explanation
framework based on adversarially generated counterfactual examples for tabular
data. A counterfactual example to a given instance in this context is defined
as a synthetically generated data point sampled from the estimated data
distribution which is treated differently by a model. The counterfactual
examples can be used to provide a black-box instance-level explanation of the
model behaviour as well as studying the regions in the input space where the
model performance deteriorates. Adversarial example generating algorithms are
extensively studied in the image and natural language processing (NLP) domains.
However, most financial data come in tabular format and naive application of
the existing techniques on this class of datasets generates unrealistic
samples. In this paper, we propose a counterfactual example generation method
capable of handling tabular data including discrete and categorical variables.
Our proposed algorithm uses a gradient-free optimization based on genetic
algorithms and therefore is applicable to any classification model.

本文提出了一个基于对抗性生成的数据方法的模型评估和解释框架，该方法可用于信贷评分系统中的机器学习算法，该算法应用于表格形式的金融数据，包括离散和分类变量，并使用基于遗传算法的无梯度优化进行优化。

PermuteAttack: 机器学习信用评分卡的反事实说明

PermuteAttack: Counterfactual Explanation of Machine Learning Credit  Scorecards

Model criticism is usually carried out by assessing if replicated data
generated under the fitted model looks similar to the observed data, see e.g.
Gelman, Carlin, Stern, and Rubin [2004, p. 165]. This paper presents a method
for latent variable models by pulling back the data into the space of latent
variables, and carrying out model criticism in that space. Making use of a
model's structure enables a more direct assessment of the assumptions made in
the prior and likelihood. We demonstrate the method with examples of model
criticism in latent space applied to factor analysis, linear dynamical systems
and Gaussian processes.

本文提出了一种基于潜在变量模型的方法，通过将数据拉回到潜在变量空间，直接评估潜在变量空间中的先验和似然假设以进行模型评估，并结合因子分析、线性动力系统和高斯过程等实例进行验证。

潜空间中的模型评价

Model Criticism in Latent Space

Probabilistic modeling is a powerful approach for analyzing empirical
information. We describe Edward, a library for probabilistic modeling. Edward's
design reflects an iterative process pioneered by George Box: build a model of
a phenomenon, make inferences about the model given data, and criticize the
model's fit to the data. Edward supports a broad class of probabilistic models,
efficient algorithms for inference, and many techniques for model criticism.
The library builds on top of TensorFlow to support distributed training and
hardware such as GPUs. Edward enables the development of complex probabilistic
models and their algorithms at a massive scale.

Edward 是一个基于 TensorFlow 构建的概率编程和贝叶斯推断库，支持广泛的概率模型、高效的推断算法和多种模型优化技术。