Large Language Models (LLMs) have emerged as powerful tools in various research domains. This article examines their potential through a literature review and firsthand experimentation. While LLMs offer benefits like cost-effectiveness and efficiency, challenges such as prompt tuning, biases, and subjectivity must be addressed. The study presents insights from experiments utilizing LLMs for qualitative analysis, highlighting successes and limitations. Additionally, it discusses strategies for mitigating challenges, such as prompt optimization techniques and leveraging human expertise. This study aligns with the 'LLMs as Research Tools' workshop's focus on integrating LLMs into HCI data work critically and ethically. By addressing both opportunities and challenges, our work contributes to the ongoing dialogue on their responsible application in research.

通过文献综述和第一手实验，本文研究了大型语言模型（LLMs）的潜力。尽管LLMs具有成本效益和高效性等优点，但也存在着诸如提示调优、偏见和主观性等挑战。该研究通过利用LLMs进行定性分析的实验提供了新的见解，强调了成功和限制。此外，本文还讨论了缓解挑战的策略，如优化提示技术和利用人类专业知识。我们的工作旨在将LLMs有机地融入人机交互数据工作，并积极促进其负责任的应用，以此回应关于LLMs在研究中负责任应用的持续对话。

由学徒到研究助理：大型语言模型推动研究