Recent developments in generative AI have shone a spotlight on
high-performance synthetic text generation technologies. The now wide
availability and ease of use of such models highlights the urgent need to
provide equally powerful technologies capable of identifying synthetic text.
With this in mind, we draw inspiration from psychological studies which suggest
that people can be driven by emotion and encode emotion in the text they
compose. We hypothesize that pretrained language models (PLMs) have an
affective deficit because they lack such an emotional driver when generating
text and consequently may generate synthetic text which has affective
incoherence i.e. lacking the kind of emotional coherence present in
human-authored text. We subsequently develop an emotionally aware detector by
fine-tuning a PLM on emotion. Experiment results indicate that our
emotionally-aware detector achieves improvements across a range of synthetic
text generators, various sized models, datasets, and domains. Finally, we
compare our emotionally-aware synthetic text detector to ChatGPT in the task of
identification of its own output and show substantial gains, reinforcing the
potential of emotion as a signal to identify synthetic text. Code, models, and
datasets are available at https: //github.com/alanagiasi/emoPLMsynth

我们通过情感学习来改进预训练语言模型 (PLMs) 生成的合成文本，实验结果表明我们的情感感知检测器在各种大小模型、数据集和领域中均取得了显著的提升，从而加强了情感作为识别合成文本的信号的潜力。

随机鹦鹉也有感受吗？通过情绪识别提高合成文本的神经检测

Do Stochastic Parrots have Feelings Too? Improving Neural Detection of  Synthetic Text via Emotion Recognition

Supervised text models are a valuable tool for political scientists but
present several obstacles to their use, including the expense of hand-labeling
documents, the difficulty of retrieving rare relevant documents for annotation,
and copyright and privacy concerns involved in sharing annotated documents.
This article proposes a partial solution to these three issues, in the form of
controlled generation of synthetic text with large language models. I provide a
conceptual overview of text generation, guidance on when researchers should
prefer different techniques for generating synthetic text, a discussion of
ethics, and a simple technique for improving the quality of synthetic text. I
demonstrate the usefulness of synthetic text with three applications:
generating synthetic tweets describing the fighting in Ukraine, synthetic news
articles describing specified political events for training an event detection
system, and a multilingual corpus of populist manifesto statements for training
a sentence-level populism classifier.

本文介绍了受控产生合成文本的方法，以解决政治科学家在使用受监督文本模型方面面临的挑战。作者用三个应用程序证明了合成文本的实用性，包括生成描述乌克兰战斗的虚拟推文，为训练事件检测系统的合成新闻文章，以及训练句子级民粹主义分类器的多语言语料库。