This paper explores the use of open generative Large Language Models (LLMs)
for annotation tasks in the social sciences. The study highlights the
challenges associated with proprietary models, such as limited reproducibility
and privacy concerns, and advocates for the adoption of open (source) models
that can be operated on independent devices. Two examples of annotation tasks,
sentiment analysis in tweets and identification of leisure activities in
childhood aspirational essays are provided. The study evaluates the performance
of different prompting strategies and models (neural-chat-7b-v3-2,
Starling-LM-7B-alpha, openchat_3.5, zephyr-7b-alpha and zephyr-7b-beta). The
results indicate the need for careful validation and tailored prompt
engineering. The study highlights the advantages of open models for data
privacy and reproducibility.

本研究探讨了在社会科学中使用开放生成的大型语言模型（LLMs）进行注释任务的用途。研究强调了专有模型所面临的限制再现性和隐私问题，并主张采用可在独立设备上运行的开放（源）模型。提供了推文情感分析和童年志向性散文中休闲活动的识别两个注释任务的示例。研究评估了不同的提示策略和模型（neural-chat-7b-v3-2，Starling-LM-7B-alpha，openchat_3.5，zephyr-7b-alpha 和 zephyr-7b-beta）的性能。结果表明需要仔细验证和定制提示工程。研究强调了开放模型在数据隐私和再现性方面的优势。