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.

翻译：大语言模型（LLMs）已逐渐成为各研究领域的有力工具。本文通过文献综述和第一手实验探究其应用潜力。尽管大语言模型具有成本效益高、效率提升等优势，但提示调优、偏差及主观性等问题仍需加以解决。本研究通过运用大语言模型展开定性分析的实验，揭示了其成功案例与局限性。此外，本文还探讨了缓解上述挑战的策略，包括提示优化技术以及发挥人类专业知识的作用。本研究与"大语言模型作为研究工具"研讨会聚焦的议题一致，旨在以批判性和伦理性的方式将大语言模型整合至人机交互数据工作中。通过同时关注机遇与挑战，本文为推进负责任地应用大语言模型的持续讨论作出了贡献。