从用户所言中读取他们的心思：基于LLM的共情心理推断研究
Reading Users' Minds from What They Say: An Investigation into LLM-based Empathic Mental Inference
AI总结：
本文研究使用大型语言模型（LLMs）执行心理推断任务以推断用户的潜在目标和基本心理需求，实验表明其表现与人类设计师相当，为增强共情设计的可扩展性提供了新途径。
摘要：
在人本设计中，发展对用户体验的全面深入理解，即共情理解，对于设计真正满足人类需求的产品至关重要。然而，准确理解大量人群真实的潜在心理状态在今天仍是一个重大挑战。这种困难主要源于用户体验研究中深度与规模之间的权衡：从一小群用户中获得的深入见解不容易扩展到更大的人群，反之亦然。本文研究使用大型语言模型（LLMs）来执行心理推断任务，具体是推断用户的潜在目标和基本心理需求（FPNs）。从人类用户和设计师处收集了基线和基准数据集，以开发共情准确度指标，用于衡量LLMs的心理推断表现。实验比较了采用不同零样本提示工程技术的不同LLMs在推断目标和FPNs时的共情准确度与人类设计师的表现。实验结果表明，LLMs能够推断并理解用户的潜在目标和FPNs，其表现与人类设计师相当，这表明通过整合先进人工智能技术来增强共情设计方法可扩展性的一条有前景的途径。这项工作有潜力显著扩充设计师在人本设计过程中可用的工具包，从而能够实现对用户体验的大规模且深入的理解。
英文摘要：
In human-centered design, developing a comprehensive and in-depth understanding of user experiences, i.e., empathic understanding, is paramount for designing products that truly meet human needs. Nevertheless, accurately comprehending the real underlying mental states of a large human population remains a significant challenge today. This difficulty mainly arises from the trade-off between depth and scale of user experience research: gaining in-depth insights from a small group of users does not easily scale to a larger population, and vice versa. This paper investigates the use of Large Language Models (LLMs) for performing mental inference tasks, specifically inferring users' underlying goals and fundamental psychological needs (FPNs). Baseline and benchmark datasets were collected from human users and designers to develop an empathic accuracy metric for measuring the mental inference performance of LLMs. The empathic accuracy of inferring goals and FPNs of different LLMs with varied zero-shot prompt engineering techniques are experimented against that of human designers. Experimental results suggest that LLMs can infer and understand the underlying goals and FPNs of users with performance comparable to that of human designers, suggesting a promising avenue for enhancing the scalability of empathic design approaches through the integration of advanced artificial intelligence technologies. This work has the potential to significantly augment the toolkit available to designers during human-centered design, enabling the development of both large-scale and in-depth understanding of users' experiences.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
