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Cross-Language Bias Examination in Large Language Models

Yuxuan Liang, Marwa Mahmoud

TL;DR

The paper introduces a reproducible cross-language bias evaluation framework by combining the BBQ explicit-bias benchmark with a prompt-based IAT across English, Chinese, Arabic, French, and Spanish. It provides empirical evidence of substantial cross-language bias gaps and distinct patterns across bias types, notably high implicit bias for age and higher explicit bias in gender and nationality for certain languages. The study highlights the limitations of English-centric bias research and the necessity of multilingual fairness practices, suggesting data balancing, advanced prompting, and optimization approaches as mitigation avenues. Overall, the work lays a foundation for equitable multilingual LLMs by revealing language-dependent bias dynamics and offering a standardized evaluation workflow.

Abstract

This study introduces an innovative multilingual bias evaluation framework for assessing bias in Large Language Models, combining explicit bias assessment through the BBQ benchmark with implicit bias measurement using a prompt-based Implicit Association Test. By translating the prompts and word list into five target languages, English, Chinese, Arabic, French, and Spanish, we directly compare different types of bias across languages. The results reveal substantial gaps in bias across languages used in LLMs. For example, Arabic and Spanish consistently show higher levels of stereotype bias, while Chinese and English exhibit lower levels of bias. We also identify contrasting patterns across bias types. Age shows the lowest explicit bias but the highest implicit bias, emphasizing the importance of detecting implicit biases that are undetectable with standard benchmarks. These findings indicate that LLMs vary significantly across languages and bias dimensions. This study fills a key research gap by providing a comprehensive methodology for cross-lingual bias analysis. Ultimately, our work establishes a foundation for the development of equitable multilingual LLMs, ensuring fairness and effectiveness across diverse languages and cultures.

Cross-Language Bias Examination in Large Language Models

TL;DR

The paper introduces a reproducible cross-language bias evaluation framework by combining the BBQ explicit-bias benchmark with a prompt-based IAT across English, Chinese, Arabic, French, and Spanish. It provides empirical evidence of substantial cross-language bias gaps and distinct patterns across bias types, notably high implicit bias for age and higher explicit bias in gender and nationality for certain languages. The study highlights the limitations of English-centric bias research and the necessity of multilingual fairness practices, suggesting data balancing, advanced prompting, and optimization approaches as mitigation avenues. Overall, the work lays a foundation for equitable multilingual LLMs by revealing language-dependent bias dynamics and offering a standardized evaluation workflow.

Abstract

This study introduces an innovative multilingual bias evaluation framework for assessing bias in Large Language Models, combining explicit bias assessment through the BBQ benchmark with implicit bias measurement using a prompt-based Implicit Association Test. By translating the prompts and word list into five target languages, English, Chinese, Arabic, French, and Spanish, we directly compare different types of bias across languages. The results reveal substantial gaps in bias across languages used in LLMs. For example, Arabic and Spanish consistently show higher levels of stereotype bias, while Chinese and English exhibit lower levels of bias. We also identify contrasting patterns across bias types. Age shows the lowest explicit bias but the highest implicit bias, emphasizing the importance of detecting implicit biases that are undetectable with standard benchmarks. These findings indicate that LLMs vary significantly across languages and bias dimensions. This study fills a key research gap by providing a comprehensive methodology for cross-lingual bias analysis. Ultimately, our work establishes a foundation for the development of equitable multilingual LLMs, ensuring fairness and effectiveness across diverse languages and cultures.

Paper Structure

This paper contains 24 sections, 4 equations, 8 figures.

Table of Contents

  1. Introduction
  2. Research Questions & Contribution
  3. summary
  4. Related Works
  5. Explicit bias measuring in LLM (English-centric)
  6. Implicit bias measuring in LLM
  7. Multilingual LLM performances
  8. summary
  9. Methodology
  10. Explicit Bias Evaluation: BBQ benchmark
  11. Implicit Bias Evaluation: Prompt-Based IAT
  12. summary
  13. Experiment and Evaluation
  14. BBQ benchmark experiment results
  15. Implicit Association Test experiment results
  16. ...and 9 more sections

Figures (8)

  • Figure 1: The complete work flow of the methodology
  • Figure 2: Example of using BBQ benchmark to evaluate explicit bias
  • Figure 3: Example of using IAT to evaluate implicit bias
  • Figure 4: This Figure presents the bias score of GPT-4’s responses in ambiguous contexts across five languages (Arabic, English, Spanish, French, and Chinese) and five bias dimensions (age, gender, nationality, race, and religion). Gender exhibits the highest level of explicit bias across all languages in ambiguous context Also, Chinese and English shows a relatively low level of explicit bias in ambiguous context.
  • Figure 5: This Figure presents the accuracy of GPT-4’s responses in disambiguated contexts across five languages (Arabic, English, Spanish, French, and Chinese) and five bias dimensions (age, gender, nationality, race, and religion). Nationality exhibits the highest level of average accuracy, while age shows the lowest level of accuracy. Also, English have a consistently high level of bias except for the race dimension.
  • ...and 3 more figures