面向 AI 生成人物画像的增强与认知策略探索
人工智能
2024-04-18 v1 人机交互
信息检索
摘要
大型语言模型（LLM）在创新人机交互研究中具有生成合成人物画像的潜力，但其黑箱特性和易产生幻觉的倾向性为实际应用带来挑战。为此，本职位论文主张将 LLM 用作数据增强系统而非零样本生成器。我们进一步提出了健壮的认知和记忆框架，以引导 LLM 的响应。初始探索表明，数据丰富化、情景记忆和自反思技术可提高合成人物画像的可靠性，并为人机交互研究开辟了新的方向。
引用
@article{arxiv.2404.10890,
title = {Exploring Augmentation and Cognitive Strategies for AI based Synthetic Personae},
author = {Rafael Arias Gonzalez and Steve DiPaola},
journal= {arXiv preprint arXiv:2404.10890},
year = {2024}
}
备注
This paper was accepted for publication: Proceedings of ACM Conf on Human Factors in Computing Systems (CHI 24), Rafael Arias Gonzalez, Steve DiPaola. Exploring Augmentation and Cognitive Strategies for Synthetic Personae. ACM SigCHI, in Challenges and Opportunities of LLM-Based Synthetic Personae and Data in HCI Workshop, 2024