This position paper explores the benefits, drawbacks, and ethical
considerations of incorporating synthetic personae in HCI research,
particularly focusing on the customization challenges beyond the limitations of
current Large Language Models (LLMs). These perspectives are derived from the
initial results of a sub-study employing vignettes to showcase the existence of
bias within black-box LLMs and explore methods for manipulating them. The study
aims to establish a foundation for understanding the challenges associated with
these models, emphasizing the necessity of thorough testing before utilizing
them to create synthetic personae for HCI research.

该研究通过使用生动事例来展示黑盒大型语言模型中的偏见存在和调控方法，探索了合成人设在人机交互研究中的益处、不足和伦理考虑，强调在利用这些模型进行合成人设创建前的全面测试的必要性。

关于使用大型语言模型创建合成人物时存在的偏见的担忧

Concerns on Bias in Large Language Models when Creating Synthetic  Personae

Large language models (LLMs) hold potential for innovative HCI research,
including the creation of synthetic personae. However, their black-box nature
and propensity for hallucinations pose challenges. To address these
limitations, this position paper advocates for using LLMs as data augmentation
systems rather than zero-shot generators. We further propose the development of
robust cognitive and memory frameworks to guide LLM responses. Initial
explorations suggest that data enrichment, episodic memory, and self-reflection
techniques can improve the reliability of synthetic personae and open up new
avenues for HCI research.

大型语言模型（LLMs）作为数据增强系统在人机交互研究中可用于创建合成人物，以解决黑盒特性和幻觉倾向带来的挑战，并提出了发展鲁棒性认知和记忆框架以指导 LLM 响应的构想。初步探索表明，数据丰富、情景记忆和自我反思技术可以提高合成人物的可靠性，为人机交互研究开辟新途径。