Decoding News Narratives: A Critical Analysis of Large Language Models in Framing Detection

• URL: http://arxiv.org/abs/2402.11621v3
• Date: Sat, 15 Jun 2024 23:20:15 GMT
• Title: Decoding News Narratives: A Critical Analysis of Large Language Models in Framing Detection
• Authors: Valeria Pastorino, Jasivan A. Sivakumar, Nafise Sadat Moosavi,
• Abstract summary: 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.
• Score: 10.301985230669684
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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