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
Anthology ID:
2024.emnlp-main.1094
Volume:
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
Month:
November
Year:
2024
Address:
Miami, Florida, USA
Editors:
Yaser Al-Onaizan, Mohit Bansal, Yun-Nung Chen
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
19617–19634
Language:
URL:
https://aclanthology.org/2024.emnlp-main.1094
DOI:
10.18653/v1/2024.emnlp-main.1094
Bibkey:
Cite (ACL):
Siyang Liu, Trisha Maturi, Bowen Yi, Siqi Shen, and Rada Mihalcea. 2024. The Generation Gap: Exploring Age Bias in the Value Systems of Large Language Models. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 19617–19634, Miami, Florida, USA. Association for Computational Linguistics.
Cite (Informal):
The Generation Gap: Exploring Age Bias in the Value Systems of Large Language Models (Liu et al., EMNLP 2024)
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PDF:
https://preview.aclanthology.org/dois-2013-emnlp/2024.emnlp-main.1094.pdf
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