Exploring Augmentation and Cognitive Strategies for AI based Synthetic Personae

• URL: http://arxiv.org/abs/2404.10890v1
• Date: Tue, 16 Apr 2024 20:22:12 GMT
• Title: Exploring Augmentation and Cognitive Strategies for AI based Synthetic Personae
• Authors: Rafael Arias Gonzalez, Steve DiPaola,
• Abstract summary: This position paper advocates for using large language models (LLMs) as data augmentation systems rather than zero-shot generators. We 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.
• Score: 1.0742675209112622
• License: http://creativecommons.org/licenses/by/4.0/
• 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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