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Research paper

Large Language Models for Biomedical Text Simplification: Promising But Not There Yet

In this system report, we describe the models and methods we used for our participation in the PLABA2023 task on biomedical abstract simplification, part of the TAC 2023 tracks. The system outputs we submitted come from the following three categories: 1) domain fine-tuned T5-like models including Biomedical-T5 and Lay-SciFive; 2) fine-tuned BARTLarge model with controllable attributes (via tokens) BART-w-CTs; 3) ChatGPTprompting. We also present the work we carried out for this task on BioGPT finetuning. In the official automatic evaluation using SARI scores, BeeManc ranks 2nd among all teams and our model LaySciFive ranks 3rd among all 13 ev

Li, Zihao, Belkadi, Samuel, Micheletti, Nicolo, Han, Lifeng, Shardlow, Matthew, Nenadic, Goran
Creative Commons Attribution Non Commercial Share Alike 4.0 InternationalView resource

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