The Generation Gap:Exploring Age Bias in Large Language Models

arXiv:2404.08760v1 Announce Type: new
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 …

abstract age alignment arxiv bias cs.ai cs.cl data diverse explore gap general language language models large language large language models leveraging data llm llms paper prompts robustness set survey through type value values world