利用访谈信息增强的大语言模型来模拟调查回复：来自AI生成数据与人类数据的比较见解
Leveraging Interview-Informed LLMs to Model Survey Responses: Comparative Insights from AI-Generated and Human Data
AI总结：
本研究探讨利用个人访谈引导大语言模型预测人类调查回复，发现访谈数据、精心设计的提示和低温度设置能增强模型与人类回复的一致性，揭示了该方法连接定性与定量方法的潜力及局限性。
摘要：
混合方法研究整合定量与定性数据，但在对齐两者不同结构时面临挑战，特别是在检查测量特征和个体回复模式方面。大语言模型（LLMs）的进展通过生成基于定性数据的合成调查回复提供了有前景的解决方案。本研究探讨LLMs在个人访谈引导下能否可靠预测人类调查回复，以运动中的行为调节问卷（BREQ）和课后项目工作人员的访谈为案例。结果表明，LLMs能捕捉总体回复模式，但变异性低于人类。纳入访谈数据改善了某些模型（如Claude、GPT）的回复多样性，而精心设计的提示和低温度设置增强了LLM与人类回复之间的一致性。人口统计信息对一致性准确性的影响小于访谈内容。这些发现强调了基于访谈的LLMs在连接定性与定量方法方面的潜力，同时揭示了在回复变异性、情感解释和心理测量保真度方面的局限性。未来研究应改进提示设计，探索偏差缓解，并优化模型设置，以增强LLM生成的调查数据在社会科学研究中的有效性。
英文摘要：
Mixed methods research integrates quantitative and qualitative data but faces challenges in aligning their distinct structures, particularly in examining measurement characteristics and individual response patterns. Advances in large language models (LLMs) offer promising solutions by generating synthetic survey responses informed by qualitative data. This study investigates whether LLMs, guided by personal interviews, can reliably predict human survey responses, using the Behavioral Regulations in Exercise Questionnaire (BREQ) and interviews from after-school program staff as a case study. Results indicate that LLMs capture overall response patterns but exhibit lower variability than humans. Incorporating interview data improves response diversity for some models (e.g., Claude, GPT), while well-crafted prompts and low-temperature settings enhance alignment between LLM and human responses. Demographic information had less impact than interview content on alignment accuracy. These findings underscore the potential of interview-informed LLMs to bridge qualitative and quantitative methodologies while revealing limitations in response variability, emotional interpretation, and psychometric fidelity. Future research should refine prompt design, explore bias mitigation, and optimize model settings to enhance the validity of LLM-generated survey data in social science research.
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。
