The Generation Gap: Exploring Age Bias in the Value Systems of Large Language Models
<|reference_start|>The Generation Gap: Exploring Age Bias in the Value Systems of Large Language Models: 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 are available at \url{ https://github.com/MichiganNLP/Age-Bias-In-LLMs}<|reference_end|>
@article{liu2024the,
    title={The Generation Gap: Exploring Age Bias in the Value Systems of Large
  Language Models},
    author={Siyang Liu, Trish Maturi, Bowen Yi, Siqi Shen, Rada Mihalcea},
    journal={The 2024 Conference on Empirical Methods in Natural Language
  Processing},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.08760},
    primaryClass={cs.CL cs.AI}
}