  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.
