生成心理测量学中生成规模发展的提示工程
Prompt Engineering for Scale Development in Generative Psychometrics
AI总结：
本文研究了提示工程策略如何影响大语言模型生成的人格评估题目质量，发现自适应提示设计在提升结构效度和保留题目池大小方面表现最佳，且其效益随模型能力增强而提升。
摘要：
本文通过蒙特卡洛模拟研究了提示工程策略如何影响生成心理测量学中大语言模型（LLM）生成的人格特质评估题目质量。针对大五特质生成题目池时，采用多种提示设计（零样本、少样本、基于角色和自适应）、模型温度和LLM进行生成，然后通过网络心理测量方法评估和缩减。在所有条件下，AI-GENIE在缩减后可靠地提高了结构效度，其增量贡献的大小与输入题目池的质量成反比。提示设计对预缩减和后缩减的题目质量均有显著影响。自适应提示设计在减少语义冗余、提升预缩减结构效度和保留更大题目池方面始终优于非自适应策略，特别是在与较新、更高容量的模型结合时。这些收益在大多数模型的温度设置下表现稳健，表明自适应提示缓解了创造力与心理测量一致性之间的常见权衡。然而，对于GPT-4o模型在高温设置下出现例外，表明模型对自适应约束的敏感性在高随机性下有所增加。总体而言，研究结果表明，自适应提示设计是本研究中最强的策略，其效益随模型能力增强而提升，推动了对生成心理测量学管道中模型-提示交互的持续研究。
英文摘要：
This Monte Carlo simulation examines how prompt engineering strategies shape the quality of large language model (LLM)--generated personality assessment items within the AI-GENIE framework for generative psychometrics. Item pools targeting the Big Five traits were generated using multiple prompting designs (zero-shot, few-shot, persona-based, and adaptive), model temperatures, and LLMs, then evaluated and reduced using network psychometric methods. Across all conditions, AI-GENIE reliably improved structural validity following reduction, with the magnitude of its incremental contribution inversely related to the quality of the incoming item pool. Prompt design exerted a substantial influence on both pre- and post-reduction item quality. Adaptive prompting consistently outperformed non-adaptive strategies by sharply reducing semantic redundancy, elevating pre-reduction structural validity, and preserving substantially larger item pool, particularly when paired with newer, higher-capacity models. These gains were robust across temperature settings for most models, indicating that adaptive prompting mitigates common trade-offs between creativity and psychometric coherence. An exception was observed for the GPT-4o model at high temperatures, suggesting model-specific sensitivity to adaptive constraints at elevated stochasticity. Overall, the findings demonstrate that adaptive prompting is the strongest approach in this context, and that its benefits scale with model capability, motivating continued investigation of model--prompt interactions in generative psychometric pipelines.
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。
