LLMs offer valuable capabilities, yet they can be utilized by malicious users
to disseminate deceptive information and generate fake news. The growing
prevalence of LLMs poses difficulties in crafting detection approaches that
remain effective across various text domains. Additionally, the absence of
precautionary measures for AI-generated news on online social platforms is
concerning. Therefore, there is an urgent need to improve people's ability to
differentiate between news articles written by humans and those produced by
LLMs. By providing cues in human-written and LLM-generated news, we can help
individuals increase their skepticism towards fake LLM-generated news. This
paper aims to elucidate simple markers that help individuals distinguish
between articles penned by humans and those created by LLMs. To achieve this,
we initially collected a dataset comprising 39k news articles authored by
humans or generated by four distinct LLMs with varying degrees of fake. We then
devise a metric named Entropy-Shift Authorship Signature (ESAS) based on the
information theory and entropy principles. The proposed ESAS ranks terms or
entities, like POS tagging, within news articles based on their relevance in
discerning article authorship. We demonstrate the effectiveness of our metric
by showing the high accuracy attained by a basic method, i.e., TF-IDF combined
with logistic regression classifier, using a small set of terms with the
highest ESAS score. Consequently, we introduce and scrutinize these top
ESAS-ranked terms to aid individuals in strengthening their skepticism towards
LLM-generated fake news.

该论文旨在通过提供简单的标记，帮助个人区分人类撰写的文章和由 LLMs 生成的文章，从而增强对 LLMs 生成的假新闻的怀疑。研究中引入并详细审查 ESAS 排名最高的词汇，以辅助个人加强对 LLMs 生成假新闻的怀疑。

透过人工智能的视角：增强人类对 LLM 生成的假新闻的怀疑

Seeing Through AI's Lens: Enhancing Human Skepticism Towards  LLM-Generated Fake News

In the realm of public health, vaccination stands as the cornerstone for
mitigating disease risks and controlling their proliferation. The recent
COVID-19 pandemic has highlighted how vaccines play a crucial role in keeping
us safe. However the situation involves a mix of perspectives, with skepticism
towards vaccines prevailing for various reasons such as political dynamics,
apprehensions about side effects, and more. The paper addresses the challenge
of comprehensively understanding and categorizing these diverse concerns
expressed in the context of vaccination. Our focus is on developing a robust
multi-label classifier capable of assigning specific concern labels to tweets
based on the articulated apprehensions towards vaccines. To achieve this, we
delve into the application of a diverse set of advanced natural language
processing techniques and machine learning algorithms including transformer
models like BERT, state of the art GPT 3.5, Classifier Chains & traditional
methods like SVM, Random Forest, Naive Bayes. We see that the cutting-edge
large language model outperforms all other methods in this context.

应对疫苗相关的多样化担忧，本文通过多种先进的自然语言处理技术和机器学习算法，包括 BERT、GPT 3.5、分类器链和传统方法如 SVM、随机森林、朴素贝叶斯，开发了一种强大的多标签分类器，能够根据与疫苗相关的担忧指定特定的关注标签。研究发现，尖端的大型语言模型在这个领域的表现超越其他方法。

解码问题：社交媒体中疫苗情感的多标签分类

Decoding Concerns: Multi-label Classification of Vaccine Sentiments in  Social Media

In this thorough study, we took a closer look at the skepticism that has
arisen with respect to potential dangers associated with artificial
intelligence, denoted as AI Risk Skepticism. Our study takes into account
different points of view on the topic and draws parallels with other forms of
skepticism that have shown up in science. We categorize the various skepticisms
regarding the dangers of AI by the type of mistaken thinking involved. We hope
this will be of interest and value to AI researchers concerned about the future
of AI and the risks that it may pose. The issues of skepticism and risk in AI
are decidedly important and require serious consideration. By addressing these
issues with the rigor and precision of scientific research, we hope to better
understand the objections we face and to find satisfactory ways to resolve
them.

本研究调查了与人工智能相关的潜在危险引起怀疑的问题，并将它与其他科学领域中出现的怀疑形式进行比较，通过针对其中蕴含的错误思考类型进行分类，旨在更好地理解我们现有的异议并寻找令人满意的解决方式。