Large language models (LLMs) have shown the ability to produce fluent and
cogent content, presenting both productivity opportunities and societal risks.
To build trustworthy AI systems, it is imperative to distinguish between
machine-generated and human-authored content. The leading zero-shot detector,
DetectGPT, showcases commendable performance but is marred by its intensive
computational costs. In this paper, we introduce the concept of conditional
probability curvature to elucidate discrepancies in word choices between LLMs
and humans within a given context. Utilizing this curvature as a foundational
metric, we present Fast-DetectGPT, an optimized zero-shot detector, which
substitutes DetectGPT's perturbation step with a more efficient sampling step.
Our evaluations on various datasets, source models, and test conditions
indicate that Fast-DetectGPT not only outperforms DetectGPT in both the
white-box and black-box settings but also accelerates the detection process by
a factor of 340, as detailed in Table 1.

介绍了一种基于条件概率曲率的新型优化零样本检测器 Fast-DetectGPT，该检测器在多个数据集、源模型和测试条件下的评估表明，在白盒和黑盒设置中，Fast-DetectGPT 不仅在 DetectGPT 之上表现出色，还将检测过程加速了 340 倍。

Fast-DetectGPT: 通过条件概率曲率高效零样本检测机器生成的文本

Fast-DetectGPT: Efficient Zero-Shot Detection of Machine-Generated Text  via Conditional Probability Curvature

As AI systems have obtained significant performance to be deployed widely in
our daily live and human society, people both enjoy the benefits brought by
these technologies and suffer many social issues induced by these systems. To
make AI systems good enough and trustworthy, plenty of researches have been
done to build guidelines for trustworthy AI systems. Machine learning is one of
the most important parts for AI systems and representation learning is the
fundamental technology in machine learning. How to make the representation
learning trustworthy in real-world application, e.g., cross domain scenarios,
is very valuable and necessary for both machine learning and AI system fields.
Inspired by the concepts in trustworthy AI, we proposed the first trustworthy
representation learning across domains framework which includes four concepts,
i.e, robustness, privacy, fairness, and explainability, to give a comprehensive
literature review on this research direction. Specifically, we first introduce
the details of the proposed trustworthy framework for representation learning
across domains. Second, we provide basic notions and comprehensively summarize
existing methods for the trustworthy framework from four concepts. Finally, we
conclude this survey with insights and discussions on future research
directions.

我们提出了第一个跨领域可信度学习框架，它包括鲁棒性、隐私、公平性和可解释性等四个概念，在这个研究方向上进行了全面的文献综述，介绍了框架的细节以及基本概念和现有方法，并对未来的研究方向进行了洞察和讨论。

域间可信度表征学习

Trustworthy Representation Learning Across Domains

In the current era, people and society have grown increasingly reliant on
artificial intelligence (AI) technologies. AI has the potential to drive us
towards a future in which all of humanity flourishes. It also comes with
substantial risks for oppression and calamity. Discussions about whether we
should (re)trust AI have repeatedly emerged in recent years and in many
quarters, including industry, academia, healthcare, services, and so on.
Technologists and AI researchers have a responsibility to develop trustworthy
AI systems. They have responded with great effort to design more responsible AI
algorithms. However, existing technical solutions are narrow in scope and have
been primarily directed towards algorithms for scoring or classification tasks,
with an emphasis on fairness and unwanted bias. To build long-lasting trust
between AI and human beings, we argue that the key is to think beyond
algorithmic fairness and connect major aspects of AI that potentially cause
AI's indifferent behavior. In this survey, we provide a systematic framework of
Socially Responsible AI Algorithms that aims to examine the subjects of AI
indifference and the need for socially responsible AI algorithms, define the
objectives, and introduce the means by which we may achieve these objectives.
We further discuss how to leverage this framework to improve societal
well-being through protection, information, and prevention/mitigation.

本文介绍了开发 Socially Responsible AI Algorithms 框架的意义和方法来实现人工智能的可信赖，从而提高人类社会的福祉。