用大模型提升质性研究效率，自动分析海量文本数据。
Large Language Model for Qualitative Research -- A Systematic Mapping Study
- 系统梳理大模型在质性研究中的应用方法与配置方式。
- 发现大模型可显著减少人工分析量，但存在准确率波动问题。
- 适合研究者与技术人员参考，推动人机协同分析发展。
医疗、教育和社科等领域文本数据呈指数级增长，传统质性分析方法因耗时长且易受主观影响，已难以应对。大语言模型（LLMs）凭借先进生成式AI能力，成为自动化与增强质性分析的变革性工具。本研究对相关文献进行系统性映射，探讨其应用场景、配置方式、方法论及评估指标。结果表明，LLMs已在多个领域广泛应用，具备自动化替代大量人工分析的潜力。然而，仍面临依赖提示工程、偶发错误及上下文理解局限等挑战。研究强调应加强人机协作、提升模型鲁棒性并优化评估方法。通过总结趋势与识别研究空白，本文旨在为未来大模型在质性分析中的应用提供指导。
原文摘要 · Abstract (English)
The exponential growth of text-based data in domains such as healthcare, education, and social sciences has outpaced the capacity of traditional qualitative analysis methods, which are time-intensive and prone to subjectivity. Large Language Models (LLMs), powered by advanced generative AI, have emerged as transformative tools capable of automating and enhancing qualitative analysis. This study systematically maps the literature on the use of LLMs for qualitative research, exploring their application contexts, configurations, methodologies, and evaluation metrics. Findings reveal that LLMs are utilized across diverse fields, demonstrating the potential to automate processes traditionally requiring extensive human input. However, challenges such as reliance on prompt engineering, occasional inaccuracies, and contextual limitations remain significant barriers. This research highlights opportunities for integrating LLMs with human expertise, improving model robustness, and refining evaluation methodologies. By synthesizing trends and identifying research gaps, this study aims to guide future innovations in the application of LLMs for qualitative analysis.
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