  Previous studies on framing have relied on manual analysis or fine-tuning
models with limited annotated datasets. However, pre-trained models, with their
diverse training backgrounds, offer a promising alternative. This paper
presents a comprehensive analysis of GPT-4, GPT-3.5 Turbo, and FLAN-T5 models
in detecting framing in news headlines. We evaluated these models in various
scenarios: zero-shot, few-shot with in-domain examples, cross-domain examples,
and settings where models explain their predictions. Our results show that
explainable predictions lead to more reliable outcomes. GPT-4 performed
exceptionally well in few-shot settings but often misinterpreted emotional
language as framing, highlighting a significant challenge. Additionally, the
results suggest that consistent predictions across multiple models could help
identify potential annotation inaccuracies in datasets. Finally, we propose a
new small dataset for real-world evaluation on headlines from a diverse set of
topics.
