The Generation Gap: Exploring Age Bias in Large Language ModelsDownload PDF

Anonymous

16 Feb 2024ACL ARR 2024 February Blind SubmissionReaders: Everyone
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. 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: Computational Social Science and Cultural Analytics
Contribution Types: Model analysis & interpretability
Languages Studied: english
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