Summary of Pitfalls Of Conversational Llms on News Debiasing, by Ipek Baris Schlicht and Defne Altiok and Maryanne Taouk and Lucie Flek
Pitfalls of Conversational LLMs on News Debiasing
by Ipek Baris Schlicht, Defne Altiok, Maryanne Taouk, Lucie Flek
First submitted to arxiv on: 9 Apr 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 A novel approach to debiasing in news editing is presented, with a focus on conversational Large Language Models (LLMs). The authors designed an evaluation checklist tailored to news editors’ perspectives and applied it to generated texts from three popular LLMs using a subset of a publicly available media bias dataset. The findings indicate that none of the LLMs are perfect in debiasing, with some models like ChatGPT introducing unnecessary changes that may impact the author’s style and create misinformation. Additionally, the study shows that LLMs do not perform as proficiently as domain experts in evaluating the quality of debiased outputs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how to make news editing more unbiased. It uses special computer models called Large Language Models (LLMs) to try to fix biased text. The researchers created a checklist for news editors and tested three popular LLMs on it. They found that none of the models are perfect, but some might even make things worse by changing the original text in ways that create misinformation. It also shows that these computer models aren’t as good at judging if something is biased or not as people who work with news every day. |
