@inproceedings{li-etal-2023-comparing-generic,
    title = "Comparing Generic and Expert Models for Genre-Specific Text Simplification",
    author = "Li, Zihao  and
      Shardlow, Matthew  and
      Alva-Manchego, Fernando",
    editor = "{\v{S}}tajner, Sanja  and
      Saggio, Horacio  and
      Shardlow, Matthew  and
      Alva-Manchego, Fernando",
    booktitle = "Proceedings of the Second Workshop on Text Simplification, Accessibility and Readability",
    month = sep,
    year = "2023",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd., Shoumen, Bulgaria",
    url = "https://preview.aclanthology.org/fix-sig-urls/2023.tsar-1.6/",
    pages = "51--67",
    abstract = "We investigate how text genre influences the performance of models for controlled text simplification. Regarding datasets from Wikipedia and PubMed as two different genres, we compare the performance of genre-specific models trained by transfer learning and prompt-only GPT-like large language models. Our experiments showed that: (1) the performance loss of genre-specific models on general tasks can be limited to 2{\%}, (2) transfer learning can improve performance on genre-specific datasets up to 10{\%} in SARI score from the base model without transfer learning, (3) simplifications generated by the smaller but more customized models show similar performance in simplicity and a better meaning reservation capability to the larger generic models in both automatic and human evaluations."
}