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

arXiv:2402.11621v1 Announce Type: new
Abstract: 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 bias. GPT-4, in …

abstract analysis arxiv bias cs.cl decoding detection few-shot gpt gpt-3 gpt-3.5 gpt-4 language language models large language large language models llms performance prompting research social social sciences through turbo type work zero-shot