@inproceedings{baris-schlicht-etal-2024-pitfalls,
    title = "Pitfalls of Conversational {LLM}s on News Debiasing",
    author = "Baris Schlicht, Ipek  and
      Altiok, Defne  and
      Taouk, Maryanne  and
      Flek, Lucie",
    editor = "Hautli-Janisz, Annette  and
      Lapesa, Gabriella  and
      Anastasiou, Lucas  and
      Gold, Valentin  and
      Liddo, Anna De  and
      Reed, Chris",
    booktitle = "Proceedings of the First Workshop on Language-driven Deliberation Technology (DELITE) @ LREC-COLING 2024",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.delite-1.4",
    pages = "33--38",
    abstract = "This paper addresses debiasing in news editing and evaluates the effectiveness of conversational Large Language Models in this task. We designed an evaluation checklist tailored to news editors{'} perspectives, obtained generated texts from three popular conversational models using a subset of a publicly available dataset in media bias, and evaluated the texts according to the designed checklist. Furthermore, we examined the models as evaluator for checking the quality of debiased model outputs. Our findings indicate that none of the LLMs are perfect in debiasing. Notably, some models, including ChatGPT, introduced unnecessary changes that may impact the author{'}s style and create misinformation. Lastly, we show that the models do not perform as proficiently as domain experts in evaluating the quality of debiased outputs.",
}

This paper addresses debiasing in news editing and evaluates the effectiveness of conversational Large Language Models in this task. We designed an evaluation checklist tailored to news editors{'} perspectives, obtained generated texts from three popular conversational models using a subset of a publicly available dataset in media bias, and evaluated the texts according to the designed checklist. Furthermore, we examined the models as evaluator for checking the quality of debiased model outputs. Our findings indicate that none of the LLMs are perfect in debiasing. Notably, some models, including ChatGPT, introduced unnecessary changes that may impact the author{'}s style and create misinformation. Lastly, we show that the models do not perform as proficiently as domain experts in evaluating the quality of debiased outputs.
Baris Schlicht, Ipek Altiok, Defne Taouk, Maryanne Flek, Lucie
Pitfalls of Conversational LLMs on News Debiasing