Exploring Augmentation and Cognitive Strategies for AI based Synthetic Personae

2024/04/16 by Rafael Arias Gonzalez, Gonzalez, Rafael Arias, Steve DiPaola +1
Computer Science · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Human-Computer Interaction (cs.HC) #I.2.7 #Information Retrieval (cs.IR) #Persona Design and Applications

paper · pdf · doi:10.48550/arxiv.2404.10890

openalex publication_date 2024/04/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

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