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        <title>Comparing Generic and Expert Models for Genre-Specific Text Simplification</title>
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            <title>Proceedings of the Second Workshop on Text Simplification, Accessibility and Readability</title>
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    <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.</abstract>
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%0 Conference Proceedings
%T Comparing Generic and Expert Models for Genre-Specific Text Simplification
%A Li, Zihao
%A Shardlow, Matthew
%A Alva-Manchego, Fernando
%Y Štajner, Sanja
%Y Saggio, Horacio
%Y Shardlow, Matthew
%Y Alva-Manchego, Fernando
%S Proceedings of the Second Workshop on Text Simplification, Accessibility and Readability
%D 2023
%8 September
%I INCOMA Ltd., Shoumen, Bulgaria
%C Varna, Bulgaria
%F li-etal-2023-comparing-generic
%X 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.
%U https://aclanthology.org/2023.tsar-1.6/
%P 51-67

Markdown (Informal)

[Comparing Generic and Expert Models for Genre-Specific Text Simplification](https://aclanthology.org/2023.tsar-1.6/) (Li et al., TSAR 2023)

• Comparing Generic and Expert Models for Genre-Specific Text Simplification (Li et al., TSAR 2023)

ACL

• Zihao Li, Matthew Shardlow, and Fernando Alva-Manchego. 2023. Comparing Generic and Expert Models for Genre-Specific Text Simplification. In Proceedings of the Second Workshop on Text Simplification, Accessibility and Readability, pages 51–67, Varna, Bulgaria. INCOMA Ltd., Shoumen, Bulgaria.