Cultural Bias and Cultural Alignment of Large Language Models (2311.14096v2)

Abstract: Culture fundamentally shapes people's reasoning, behavior, and communication. As people increasingly use generative AI to expedite and automate personal and professional tasks, cultural values embedded in AI models may bias people's authentic expression and contribute to the dominance of certain cultures. We conduct a disaggregated evaluation of cultural bias for five widely used LLMs (OpenAI's GPT-4o/4-turbo/4/3.5-turbo/3) by comparing the models' responses to nationally representative survey data. All models exhibit cultural values resembling English-speaking and Protestant European countries. We test cultural prompting as a control strategy to increase cultural alignment for each country/territory. For recent models (GPT-4, 4-turbo, 4o), this improves the cultural alignment of the models' output for 71-81% of countries and territories. We suggest using cultural prompting and ongoing evaluation to reduce cultural bias in the output of generative AI.

Cultural Bias in LLMs: A Comprehensive Audit and Mitigation Strategy

The paper "Auditing and Mitigating Cultural Bias in LLMs" presents a meticulous analysis of cultural bias in LLMs, specifically focusing on OpenAI's consecutive iterations of GPT—GPT-3, GPT-3.5, and GPT-4. This work evaluates the extent to which these models, when prompted in English, encode cultural values aligned with English-speaking and Protestant European countries. The paper also proposes and assesses the efficacy of cultural prompting as a strategy to mitigate this bias, utilizing the World Values Survey (WVS) and the European Values Study (EVS) to benchmark cultural alignment.

Key Findings and Methodological Approach

The authors audit the cultural responses of GPT models by juxtaposing them against empirical cultural data from the Integrated Values Surveys (IVS), which incorporate both the WVS and EVS datasets. GPT models were evaluated on their representation of the Inglehart-Welzel Cultural Map's two core dimensions: survival versus self-expression values and traditional versus secular-rational values. The analysis confirmed a pronounced bias in GPT models towards self-expression values, distinct from many cultures' survival values, indicating the models' innate cultural orientation towards individualistic, liberal principles.

The paper introduces "cultural prompting" as a mitigation strategy to reduce GPT's inherent cultural bias. This technique involves instructing the LLM to generate responses tailored to cultural norms of specific countries or territories. The strategy showed considerable reduction in the cultural bias of GPT-3.5 and GPT-4, decreasing the average cultural distance from IVS benchmarks (p < 0.001), although its efficacy was not uniform across all regions. Specifically, the effectiveness of cultural prompting was limited for certain cultural regions, such as Confucian and Orthodox European countries, indicating potential variance in LLMs' contextual comprehension and response generation capabilities.

Implications and Future Directions

The results underscore the complexity and necessity of addressing cultural bias in LLMs, particularly given their broad application in diverse socio-cultural contexts. The inherent bias towards self-expression and secular values can influence users' expression in AI-mediated communication, potentially leading to misalignment with cultural expectations and impacting interpersonal trust and professional communication. The findings imply a cautionary approach to integrating LLMs into environments where cultural sensitivity is paramount.

Practically, the paper advocates for continuous auditing of cultural bias in LLMs and the incorporation of cultural prompting in user interactions, enabling users to better align AI outputs with culturally diverse values. Theoretically, it presents a framework for understanding and contextualizing cultural bias in AI, inviting further exploration into the interplay between cultural cognition, LLM training data, and filtered outputs.

Looking forward, the research prompts further paper into the impacts of prompt language, phrasing, and their implicit influence on LLMs' performance in contextually diverse environments. It encourages similar audit methodologies across other emerging LLMs, promoting a standardized discourse on cultural audit and bias mitigation within AI systems.

In conclusion, this paper builds on the discourse of cultural bias in AI, offering both a diagnostic lens and a partial remedy via cultural prompts. As AI continues to infiltrate global communication channels, incorporating culturally aware practices into AI development and deployment will be crucial in navigating the nuanced landscape of cross-cultural interaction.