LLM助研需警惕黑箱，关键在参数与研究伦理的平衡
LLMs in Qualitative Research: Opportunities, Limitations, and Practical Considerations
- 聚焦上下文窗口、采样参数等技术细节，提升模型可控性
- 强调研究者需保持反思性与判断力，避免盲信模型输出
- 适合关注人机协作研究伦理的社科与教育领域学者
本文从质性研究方法与可解释人工智能的多学科视角，探讨大型语言模型（LLMs）在质性研究中的机遇、局限与实践考量。文章指出，负责任地将LLM融入研究流程，要求研究者审慎对待一系列技术参数，包括上下文窗口限制、温度与top-p采样设置、用户与系统提示设计，以及以系统卡片形式呈现的模型文档。这些考量需置于质性研究的核心理念——反思性、立场性与诠释判断——之中，并讨论当代LLM的不透明性相较于早期自然语言处理工具（如主题模型、词典情感分析器）的差异。
原文摘要 · Abstract (English)
This paper examines the opportunities, limitations, and practical considerations associated with the use of large language models (LLMs) in qualitative research. Drawing on a multidisciplinary perspective that combines expertise in qualitative methods and explainable AI, the paper argues that responsible integration of LLMs into qualitative workflows requires researchers to engage critically with a curated set of technical parameters, that is, context window constraints, temperature and top-p sampling settings, user and system prompt design, and model documentation in the form of system cards. The paper situates these considerations within the epistemological commitments of qualitative research, including reflexivity, positionality, and interpretive judgment, and discusses how the opacity of contemporary LLMs differs from earlier natural language processing tools such as topic models and lexicon-based sentiment analyzers.
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