相关实验视频

Updated: May 14, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

465

用贝叶斯假设测试来检测大型语言模型的隐性偏差

Shijing Si1, Xiaoming Jiang2,3, Qinliang Su4,5

  • 1School of Economics and Finance, Shanghai International Studies University, Shanghai, 201620, China.

Scientific reports
|April 11, 2025
PubMed
概括

本研究引入了一种新的假设测试框架,用于检测大型语言模型 (LLM) 中的社会偏见. 贝叶斯因子有效量化偏见证据,优于传统的统计测试.

关键词:
贝叶斯因子是一个贝叶斯因子.公平的 公平的 公平的群体偏见 群体偏见 群体偏见大型语言模型.

更多相关视频

Post-Movie Subliminal Measurement PMSM, for Investigating Implicit Social Bias09:03

Post-Movie Subliminal Measurement PMSM, for Investigating Implicit Social Bias

Published on: February 29, 2020

5.7K
Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms07:31

Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms

Published on: February 8, 2019

6.5K

相关概念视频

Types of Hypothesis Testing01:11

Types of Hypothesis Testing

There are three types of hypothesis tests: right-tailed, left-tailed, and two-tailed.
When the null and alternative hypotheses are stated, it is observed that the null hypothesis is a neutral statement against which the alternative hypothesis is tested. The alternative hypothesis is a claim that instead has a certain direction. If the null hypothesis claims that p = 0.5, the alternative hypothesis would be an opposing statement to this and can be put either p > 0.5, p < 0.5, or p...
25.8K
Statistical Hypothesis Testing01:16

Statistical Hypothesis Testing

Hypothesis testing is a critical statistical procedure facilitating informed, evidence-based decisions. It begins with a hypothesis, which is a tentative explanation, or a prediction about a population parameter. This hypothesis can be either a null hypothesis (H0), indicating no effect or difference, or an alternative hypothesis (Ha), suggesting an effect or difference.
Statistical significance measures the probability that an observed result occurred by chance. If this probability, known as...
1.8K
Stereotypes, Prejudice, and Discrimination02:55

Stereotypes, Prejudice, and Discrimination

Humans are very diverse and although we share many similarities, we also have many differences. The social groups we belong to help form our identities (Tajfel, 1974). These differences may be difficult for some people to reconcile, which may lead to prejudice toward people who are different. Prejudice is a negative attitude and feeling toward an individual based solely on one’s membership in a particular social group (Allport, 1954; Brown, 2010). Prejudice is common against people who...
89.7K
Hypothesis: Accept or Fail to Reject?01:17

Hypothesis: Accept or Fail to Reject?

The outcome of any hypothesis testing leads to rejecting or not rejecting the null hypothesis. This decision is taken based on the analysis of the data, an appropriate test statistic, an appropriate confidence level, the critical values, and P-values. However, when the evidence suggests that the null hypothesis cannot be rejected, is it right to say, 'Accept' the null hypothesis?
There are two ways to indicate that the null hypothesis is not rejected. 'Accept' the null...
27.4K
Accuracy and Errors in Hypothesis Testing01:13

Accuracy and Errors in Hypothesis Testing

Hypothesis testing is a fundamental statistical tool that begins with the assumption that the null hypothesis H0 is true. During this process, two types of errors can occur: Type I and Type II. A Type I error refers to the incorrect rejection of a true null hypothesis, while a Type II error involves the failure to reject a false null hypothesis.
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5%...
157
Confirmation Biases01:31

Confirmation Biases

The confirmation bias is the tendency to focus on information that confirms our existing beliefs and ignore information that is inconsistent with our expectations. For example, if you think that your professor is not very nice, you notice all of the instances of rude behavior exhibited by the professor while ignoring the countless pleasant interactions he is involved in on a daily basis. Have you ever fallen prey to the confirmation bias, either as the source or target of such bias?
5.4K