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2024-02-26 08:30:41

This https://arxiv.org/abs/2402.11621 has been replaced.
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Decoding News Narratives: A Critical Analysis of Large Language Models in Framing Bias Detection
This work contributes to the expanding research on the applicability of LLMs in social sciences by examining the performance of GPT-3.5 Turbo, GPT-4, and Flan-T5 models in detecting framing bias in news headlines through zero-shot, few-shot, and explainable prompting methods. A key insight from our evaluation is the notable efficacy of explainable prompting in enhancing the reliability of these models, highlighting the importance of explainable settings for social science research on framing bi…