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ChatGPT v.s. media bias: A comparative study of GPT-3.5 and fine-tuned language models
- 1 Shenzhen College of International Education
- 2 Duke University
* Author to whom correspondence should be addressed.
Abstract
In our rapidly evolving digital sphere, the ability to discern media bias becomes crucial as it can shape public sentiment and influence pivotal decisions. The advent of large language models (LLMs), such as ChatGPT, noted for their broad utility in various natural language processing (NLP) tasks, invites exploration of their efficacy in media bias detection. Can ChatGPT detect media bias? This study seeks to answer this question by leveraging the Media Bias Identification Benchmark (MBIB) to assess ChatGPT's competency in distinguishing six categories of media bias, juxtaposed against fine-tuned models such as Bidirectional and Auto-Regressive Transformers (BART), Convolutional Bidirectional Encoder Representations from Transformers (ConvBERT), and Generative Pre-trained Transformer 2 (GPT-2). The findings present a dichotomy: ChatGPT performs at par with fine-tuned models in detecting hate speech and text-level context bias, yet faces difficulties with subtler elements of other bias detections, namely, fake news, racial, gender, and cognitive biases.
Keywords
media bias detection, large language model, comparative analysis
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Cite this article
Wen,Z.;Younes,R. (2023).ChatGPT v.s. media bias: A comparative study of GPT-3.5 and fine-tuned language models.Applied and Computational Engineering,21,249-257.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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