一个模型独立的冗余度衡量人类与ChatGPT作者歧视,使用贝叶斯概率方法
Silvia Bozza1,2, Claude-Alain Roten3, Antoine Jover3,4
1Ca' Foscari University of Venice, Department of Economics, Venice, 30121, Italy. silvia.bozza@unive.it.
Scientific reports
|November 6, 2023
概括
检测人工智能生成的文本至关重要. 这项研究引入了一种新的AI模型独立方法,使用贝叶斯因子来分析文本冗余性,有效地区分人类编写的内容和人工智能 (AI) 输出,如ChatGPT.
科学领域:
- 计算语言学 计算语言学
- 法医语言学 法医语言学
- 造型测量仪使用的方法
背景情况:
- 越来越担心在学术和科学写作中未经声明使用人工智能 (AI),如ChatGPT.
- 难以验证学生提交和科学文章的作者身份.
- 需要可靠的方法来检测人工智能生成的内容.
研究的目的:
- 开发一种独立于人工智能模型的测量方法,用于区分人类撰写的文本和人工智能生成的文本.
- 量化人类和人工智能写作风格之间的语法差异.
- 在多语言背景下实施对作者归因的概率方法.
主要方法:
- 开发一个AI模型独立的冗余性措施.
- 语法差异的量化,即使对短文本也有效 (约. 1,800个字符). 这是一个很长的时间.
- 使用贝叶斯因子进行分类的贝叶斯概率方法的实施.
主要成果:
- 在英语和法语中成功区分人类和人工智能生成的文本 (包括ChatGPT).
- 可能的作者歧视与有限的错误分类率,即使在短文本.
- 即使采用小样本尺寸,模型性能也表现出令人满意的表现.
结论:
- 提出的贝叶斯概率学方法提供了一种原创且有效的风度学方法.
- 冗余措施和贝叶斯因子为作者身份验证提供了强有力的标准.
- 该方法适用于各种科学和人文领域的多语言文本.
相关概念视频
Quantifying and Rejecting Outliers: The Grubbs Test
1.6K
Sometimes, a data set can have a recorded numerical observation that greatly deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier. To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
1.6K
Probability Laws
40.9K
Overview
40.9K
The Representativeness Heuristic
15.8K
The representative heuristic describes a biased way of thinking, in which you unintentionally stereotype someone or something. For example, you may assume that your professors spend their free time reading books and engaging in intellectual conversation, because the idea of them spending their time playing volleyball or visiting an amusement park does not fit in with your stereotypes of professors.
15.8K
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data
135
Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance,...
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance,...
135
Stereotype Content Model
14.7K
The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
14.7K
Multiple Allele Traits
34.3K
The Concept of Multiple Allelism
34.3K


