The Generation Gap: Exploring Age Bias Underlying in the Value Systems of Large Language Models
Abstract: We explore the alignment of values in Large Language Models (LLMs) with specific age groups, leveraging data from the World Value Survey across thirteen categories.
Through a diverse set of prompts tailored to ensure response robustness, we find a general inclination of LLM values towards younger demographics, especially in the US.
Additionally, we explore the impact of incorporating age identity information in prompts and observe challenges in mitigating value discrepancies with different age cohorts. Our findings highlight the age bias in LLMs and provide insights for future work. Materials for our analysis will be available via \url{anonymous.github.com}
Paper Type: Short
Research Area: Ethics, Bias, and Fairness
Research Area Keywords: age bias of LLMs, model analysis, large language models
Contribution Types: Model analysis & interpretability
Languages Studied: english
Submission Number: 4159
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