The Generation Gap: Exploring Age Bias in the Value Systems of Large Language Models
Siyang Liu, Trisha Maturi, Bowen Yi, Siqi Shen, Rada Mihalcea
Abstract
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 when compared to the US population. Although a general inclination can be observed, we also found that this inclination toward younger groups can be different across different value categories. 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. Materials for our analysis will be available via https://github.com/anonymous
BibTeX
@inproceedings{liu-etal-2024-generation-gap,
title = "The Generation Gap: Exploring Age Bias in the Value Systems of Large Language Models",
author = "Liu, Siyang and
Maturi, Trisha and
Yi, Bowen and
Shen, Siqi and
Mihalcea, Rada",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.emnlp-main.1094/",
doi = "10.18653/v1/2024.emnlp-main.1094",
pages = "19617--19634"
}