BioMamba: A Pre-trained Biomedical Language Representation Model Leveraging Mamba
<|reference_start|>BioMamba: A Pre-trained Biomedical Language Representation Model Leveraging Mamba: The advancement of natural language processing (NLP) in biology hinges on models' ability to interpret intricate biomedical literature. Traditional models often struggle with the complex and domain-specific language in this field. In this paper, we present BioMamba, a pre-trained model specifically designed for biomedical text mining. BioMamba builds upon the Mamba architecture and is pre-trained on an extensive corpus of biomedical literature. Our empirical studies demonstrate that BioMamba significantly outperforms models like BioBERT and general-domain Mamba across various biomedical tasks. For instance, BioMamba achieves a 100 times reduction in perplexity and a 4 times reduction in cross-entropy loss on the BioASQ test set. We provide an overview of the model architecture, pre-training process, and fine-tuning techniques. Additionally, we release the code and trained model to facilitate further research.<|reference_end|>
@article{yue2024biomamba:,
    title={BioMamba: A Pre-trained Biomedical Language Representation Model
  Leveraging Mamba},
    author={Ling Yue, Sixue Xing, Yingzhou Lu, Tianfan Fu},
    journal={arXiv preprint arXiv:2408.02600},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.02600},
    primaryClass={cs.CL}
}