The Generation Gap:exploring Age Bias In The Underlying Value Systems Of Large Language Models

Liu Siyang, Maturi Trish, Yi Bowen, Shen Siqi, Mihalcea Rada. Arxiv 2024

[Paper]    
Ethics And Bias Prompting RAG Reinforcement Learning Security Survey Paper

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.

Similar Work