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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2024
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| Online Access: | https://arxiv.org/abs/2408.03871 |
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| _version_ | 1866910659511320576 |
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| author | Li, Zihao Belkadi, Samuel Micheletti, Nicolo Han, Lifeng Shardlow, Matthew Nenadic, Goran |
| author_facet | Li, Zihao Belkadi, Samuel Micheletti, Nicolo Han, Lifeng Shardlow, Matthew Nenadic, Goran |
| contents | 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 evaluated systems. In the official human evaluation, our model BART-w-CTs ranks 2nd on Sentence-Simplicity (score 92.84), 3rd on Term-Simplicity (score 82.33) among all 7 evaluated systems; It also produced a high score 91.57 on Fluency in comparison to the highest score 93.53. In the second round of submissions, our team using ChatGPT-prompting ranks the 2nd in several categories including simplified term accuracy score 92.26 and completeness score 96.58, and a very similar score on faithfulness score 95.3 to re-evaluated PLABA-base-1 (95.73) via human evaluations. Our codes, fine-tuned models, prompts, and data splits from the system development stage will be available at https://github.com/ HECTA-UoM/PLABA-MU |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2408_03871 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Large Language Models for Biomedical Text Simplification: Promising But Not There Yet Li, Zihao Belkadi, Samuel Micheletti, Nicolo Han, Lifeng Shardlow, Matthew Nenadic, Goran Computation and Language Artificial Intelligence 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 evaluated systems. In the official human evaluation, our model BART-w-CTs ranks 2nd on Sentence-Simplicity (score 92.84), 3rd on Term-Simplicity (score 82.33) among all 7 evaluated systems; It also produced a high score 91.57 on Fluency in comparison to the highest score 93.53. In the second round of submissions, our team using ChatGPT-prompting ranks the 2nd in several categories including simplified term accuracy score 92.26 and completeness score 96.58, and a very similar score on faithfulness score 95.3 to re-evaluated PLABA-base-1 (95.73) via human evaluations. Our codes, fine-tuned models, prompts, and data splits from the system development stage will be available at https://github.com/ HECTA-UoM/PLABA-MU |
| title | Large Language Models for Biomedical Text Simplification: Promising But Not There Yet |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2408.03871 |