BioMamba: Domain-Adaptive Biomedical Language Models
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2024
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866917350549225472 |
|---|---|
| author | Yue, Ling Zhu, Mingzhi Xing, Sixue Pan, Shaowu Chenthamarakshan, Vijil Wang, Yanbo Cao, Yunning Das, Payel Fu, Tianfan |
| author_facet | Yue, Ling Zhu, Mingzhi Xing, Sixue Pan, Shaowu Chenthamarakshan, Vijil Wang, Yanbo Cao, Yunning Das, Payel Fu, Tianfan |
| contents | Background: Biomedical language models should improve performance on biomedical text while retaining general-domain language ability. For Mamba-based models, this trade-off has not been clearly studied across biomedical literature and clinical text. Methods: We developed BioMamba, a family of biomedical models obtained by continued pretraining of public Mamba2 checkpoints on PubMed, with small amounts of general-domain data from the Colossal Clean Crawled Corpus (C4) and Wikipedia included to help preserve general-domain language ability. We evaluated language modeling and three downstream tasks across multiple model scales: clinical note completion, discharge summary generation, and biomedical yes/no question answering. Results: BioMamba consistently improved PubMed modeling, improved Wikipedia modeling, and left C4 performance largely unchanged. After supervised fine-tuning, BioMamba transferred well to both biomedical literature and clinical text, yielding strong results on completion, summarization, and question answering. On MIMIC-IV, BioMamba+SFT consistently matched or exceeded SFT from the corresponding base checkpoints across note completion and discharge summary generation. The strongest model achieved a PubMed perplexity of 5.28 and accuracies of 90.24% and 73.00% on BioASQ and PubMedQA, respectively. Conclusion: Balanced domain-adaptive pretraining strategy strengthens Mamba language models for both biomedical literature and clinical text, while preserving general-domain language capabilities, establishing BioMamba as a practical foundation for biomedical NLP applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_02600 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | BioMamba: Domain-Adaptive Biomedical Language Models Yue, Ling Zhu, Mingzhi Xing, Sixue Pan, Shaowu Chenthamarakshan, Vijil Wang, Yanbo Cao, Yunning Das, Payel Fu, Tianfan Computation and Language Background: Biomedical language models should improve performance on biomedical text while retaining general-domain language ability. For Mamba-based models, this trade-off has not been clearly studied across biomedical literature and clinical text. Methods: We developed BioMamba, a family of biomedical models obtained by continued pretraining of public Mamba2 checkpoints on PubMed, with small amounts of general-domain data from the Colossal Clean Crawled Corpus (C4) and Wikipedia included to help preserve general-domain language ability. We evaluated language modeling and three downstream tasks across multiple model scales: clinical note completion, discharge summary generation, and biomedical yes/no question answering. Results: BioMamba consistently improved PubMed modeling, improved Wikipedia modeling, and left C4 performance largely unchanged. After supervised fine-tuning, BioMamba transferred well to both biomedical literature and clinical text, yielding strong results on completion, summarization, and question answering. On MIMIC-IV, BioMamba+SFT consistently matched or exceeded SFT from the corresponding base checkpoints across note completion and discharge summary generation. The strongest model achieved a PubMed perplexity of 5.28 and accuracies of 90.24% and 73.00% on BioASQ and PubMedQA, respectively. Conclusion: Balanced domain-adaptive pretraining strategy strengthens Mamba language models for both biomedical literature and clinical text, while preserving general-domain language capabilities, establishing BioMamba as a practical foundation for biomedical NLP applications. |
| title | BioMamba: Domain-Adaptive Biomedical Language Models |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2408.02600 |