探索人与大语言模型协同在推动理论驱动的定性分析中的作用
Exploring the Human-LLM Synergy in Advancing Theory-driven Qualitative Analysis
AI总结：
本文提出CHALET方法，通过人与大语言模型的协同合作，推动理论驱动的定性分析，揭示心理健康污名的潜在主题。
摘要：
定性编码是人机交互（HCI）领域中一种具有挑战性但至关重要的研究方法。尽管近期研究已展示出大语言模型（LLMs）在理论框架内执行定性编码的能力，但其在人与LLM协同发现和生成超出初始理论的新见解方面的潜力仍被低估。为弥合这一差距，我们提出了CHALET，一种新的方法，通过利用人与LLM的合作来推动理论驱动的定性分析，通过促进迭代编码、分歧分析和定性数据的概念化。我们通过将CHALET应用于与心理健康污名相关的对话定性分析，使用归因模型作为理论框架，展示了CHALET的实用性。结果突显了人与LLM协作在揭示污名在认知、情感和行为维度下的潜在主题方面的独特贡献。我们讨论了人与LLM协同方法在理论驱动的定性分析方法论上的影响，为HCI社区及其他领域提供参考。
英文摘要：
Qualitative coding is a demanding yet crucial research method in the field of Human-Computer Interaction (HCI). While recent studies have shown the capability of large language models (LLMs) to perform qualitative coding within theoretical frameworks, their potential for collaborative human-LLM discovery and generation of new insights beyond initial theory remains underexplored. To bridge this gap, we proposed CHALET, a novel approach that harnesses the power of human-LLM partnership to advance theory-driven qualitative analysis by facilitating iterative coding, disagreement analysis, and conceptualization of qualitative data. We demonstrated CHALET's utility by applying it to the qualitative analysis of conversations related to mental-illness stigma, using the attribution model as the theoretical framework. Results highlighted the unique contribution of human-LLM collaboration in uncovering latent themes of stigma across the cognitive, emotional, and behavioral dimensions. We discuss the methodological implications of the human-LLM collaborative approach to theory-based qualitative analysis for the HCI community and beyond.
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