Summary of Chatgpt V.s. Media Bias: a Comparative Study Of Gpt-3.5 and Fine-tuned Language Models, by Zehao Wen and Rabih Younes
ChatGPT v.s. Media Bias: A Comparative Study of GPT-3.5 and Fine-tuned Language Models
by Zehao Wen, Rabih Younes
First submitted to arxiv on: 29 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper investigates whether large language models like ChatGPT can detect media bias. The study uses the Media Bias Identification Benchmark (MBIB) to evaluate ChatGPT’s performance in identifying six categories of media bias, including hate speech, text-level context bias, fake news, racial, gender, and cognitive biases. The results show that ChatGPT performs similarly to fine-tuned models like BART, ConvBERT, and GPT-2 in detecting hate speech and text-level context bias, but struggles with subtler forms of media bias. This study aims to assess the efficacy of ChatGPT in media bias detection. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at whether a special kind of computer program called ChatGPT can tell if news is biased or not. They use a test called MBIB to see how good ChatGPT is at finding different kinds of biases in news articles. The results show that ChatGPT is pretty good at finding two types of bias, but struggles with the others. This study tries to figure out if ChatGPT can really help us detect biased news. |
Keywords
» Artificial intelligence » Gpt
