UPDATE

2025/05/28: shared the prompt materials we manually pulled off from the World Value Survey, hoping to facilitate future research

Age-Bias-In-LLMs

Reference

@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",
    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 \url{https://github.com/anonymous}"
}