Generative linguistic steganography attempts to hide secret messages into
covertext. Previous studies have generally focused on the statistical
differences between the covertext and stegotext, however, ill-formed stegotext
can readily be identified by humans. In this paper, we propose a novel
zero-shot approach based on in-context learning for linguistic steganography to
achieve better perceptual and statistical imperceptibility. We also design
several new metrics and reproducible language evaluations to measure the
imperceptibility of the stegotext. Our experimental results indicate that our
method produces $1.926\times$ more innocent and intelligible stegotext than any
other method.

本研究提出了一种基于上下文学习的零样本方法，用于语言隐藏，以更好地实现感知和统计上的不可察觉性，并设计了几个新的度量标准和可复现的语言评估方法来衡量隐藏文本的隐晦性。实验结果表明，该方法产生了比其他任何方法更多的无罪和可理解的隐藏文本。

零封装生成型语言隐写术

Zero-shot Generative Linguistic Steganography

Paraphrasing is the task of expressing an essential idea or meaning in
different words. But how different should the words be in order to be
considered an acceptable paraphrase? And can we exclusively use automated
metrics to evaluate the quality of a paraphrase? We attempt to answer these
questions by conducting experiments on a German data set and performing
automatic and expert linguistic evaluation.

汤姆的研究使用德国数据集对改写的质量进行了自动和专家语言学评估，以回答改写应该有多大的差异才能被视为可接受，以及是否可以仅使用自动化指标评估改写质量的问题。

什么是好的改写：自动评估是否有效？

What Makes a Good Paraphrase: Do Automated Evaluations Work?

In the age of large transformer language models, linguistic evaluation play
an important role in diagnosing models' abilities and limitations on natural
language understanding. However, current evaluation methods show some
significant shortcomings. In particular, they do not provide insight into how
well a language model captures distinct linguistic skills essential for
language understanding and reasoning. Thus they fail to effectively map out the
aspects of language understanding that remain challenging to existing models,
which makes it hard to discover potential limitations in models and datasets.
In this paper, we introduce Curriculum as a new format of NLI benchmark for
evaluation of broad-coverage linguistic phenomena. Curriculum contains a
collection of datasets that covers 36 types of major linguistic phenomena and
an evaluation procedure for diagnosing how well a language model captures
reasoning skills for distinct types of linguistic phenomena. We show that this
linguistic-phenomena-driven benchmark can serve as an effective tool for
diagnosing model behavior and verifying model learning quality. In addition,
our experiments provide insight into the limitation of existing benchmark
datasets and state-of-the-art models that may encourage future research on
re-designing datasets, model architectures, and learning objectives.

本文介绍一种新的 NLI 基准 Curriculum，其中包括 36 种广泛涵盖的语言现象的数据集和评估程序，证明这种以语言现象驱动的基准在诊断模型行为和验证模型学习质量方面具有有效性，同时为未来对数据集的重新设计、模型架构和学习目标的研究提供了启示和借鉴。