The Generation Gap:Exploring Age Bias in Large Language Models
Siyang Liu Trish Maturi Siqi Shen Rada Mihalcea
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.