Exploring Augmentation and Cognitive Strategies for AI based Synthetic Personae
This work addresses the problem of unreliable synthetic personae in HCI research, but it is incremental as it builds on existing ideas without presenting new empirical results.
The paper tackles the challenges of using large language models for creating synthetic personae by proposing to use them as data augmentation systems and developing cognitive frameworks, with initial explorations suggesting improvements in reliability.
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.