The Generation Gap: Exploring Age Bias Underlying in the Value Systems of Large Language Models
Abstract: In this paper, 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.
Paper Type: Short
Research Area: Ethics, Bias, and Fairness
Research Area Keywords: Age bias, social bias, values of LLMs
Contribution Types: Model analysis & interpretability, Data analysis
Languages Studied: English
Section 2 Permission To Publish Peer Reviewers Content Agreement: Authors grant permission for ACL to publish peer reviewers' content
Submission Number: 894
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