Delving into Multilingual Ethical Bias: The MSQAD with Statistical Hypothesis Tests for Large Language Models

• URL: http://arxiv.org/abs/2505.19121v2
• Date: Wed, 02 Jul 2025 06:15:45 GMT
• Title: Delving into Multilingual Ethical Bias: The MSQAD with Statistical Hypothesis Tests for Large Language Models
• Authors: Seunguk Yu, Juhwan Choi, Youngbin Kim,
• Abstract summary: This paper investigates the validation and comparison of the ethical biases of LLMs concerning globally discussed and potentially sensitive topics.<n>We collected news articles from Human Rights Watch covering 17 topics, and generated socially sensitive questions along with corresponding responses in multiple languages.<n>We scrutinized the biases of these responses across languages and topics, employing two statistical hypothesis tests.
• Score: 7.480124826347168
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Despite the recent strides in large language models, studies have underscored the existence of social biases within these systems. In this paper, we delve into the validation and comparison of the ethical biases of LLMs concerning globally discussed and potentially sensitive topics, hypothesizing that these biases may arise from language-specific distinctions. Introducing the Multilingual Sensitive Questions & Answers Dataset (MSQAD), we collected news articles from Human Rights Watch covering 17 topics, and generated socially sensitive questions along with corresponding responses in multiple languages. We scrutinized the biases of these responses across languages and topics, employing two statistical hypothesis tests. The results showed that the null hypotheses were rejected in most cases, indicating biases arising from cross-language differences. It demonstrates that ethical biases in responses are widespread across various languages, and notably, these biases were prevalent even among different LLMs. By making the proposed MSQAD openly available, we aim to facilitate future research endeavors focused on examining cross-language biases in LLMs and their variant models.

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