Computational Molecular Biology 2025, Vol.15, No.4, 160-170 http://bioscipublisher.com/index.php/cmb 169 framework for knowledge representation - one that can both understand the ambiguity of semantics and maintain the rigor of structured reasoning. Think more boldly. In the future, models may no longer merely "answer questions", but be able to directly communicate with knowledge bases. When researchers input a natural language question, the model can not only retrieve the answer from the huge database, but also automatically supplement it to the knowledge graph after obtaining new discoveries. Moreover, this update process is self-checking. The model will determine by itself whether the new information is consistent with the old knowledge. This human-machine collaborative way of knowledge update can keep scientific databases "fresh in real time", especially in a rapidly changing field like biology, which is of great significance. In the long run, the changes brought about by large language models may not only be at the technical level, but also a transformation in the way research is conducted. The speed of knowledge accumulation will accelerate. Researchers will no longer spend a lot of time screening literature but rather think more about how to utilize existing knowledge to innovate and verify. AI assistants might become a standard feature in laboratories. They won't replace scientists, but they will change the pace of their work. However, the more powerful a tool is, the more it needs to be used correctly. Knowledge is a kind of power, and if power lacks restraint, it may be abused. In the future, when building an AI knowledge base, the scientific research community needs to always be vigilant about ethical risks and maintain openness and transparency. We are standing at the threshold of an era brimming with imagination - from laboratory notes to computer screens, the ways knowledge is acquired and disseminated are being rewritten. As long as the scientific spirit and ethical bottom line remain firm, this scientific research revolution jointly written by humans and large language models will eventually bring about a new chapter in the field of biology. Acknowledgments The authors extend sincere thanks to two anonymous peer reviewers for their invaluable feedback on the manuscript. Conflict of Interest Disclosure The authors affirm that this research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest. References Beltagy I., Lo K., and Cohan A., 2019, SciBERT: A pretrained language model for scientific text, In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing (EMNLP-IJCNLP), pp.3615-3620. https://doi.org/10.18653/v1/D19-1371 Wiggins W., and Tejani A., 2021, On the opportunities and risks of foundation models, Radiology: Artificial Intelligence, 2022, 4(4): e220119. https://doi.org/10.1148/ryai.220119 Brown T.B., Mann B., Ryder N., Subbiah M., Kaplan J., Dhariwal P., Neelakantan A., Shyam P., Sastry G., Askell A., Agarwal S., Herbert-Voss A., Krueger G., Henighan T., Child R., Ramesh A., Ziegler D., Wu J., Winter C., Hesse C., Chen M., Sigler E., Litwin M., Gray S., Chess B., Clark J., Berner C., McCandlish S., Radford A., Sutskever I., and Amodei D., 2020, Language models are few-shot learners, Advances in Neural Information Processing Systems, 33: 1877-1901. Devlin J., Chang M.W., Lee K., and Toutanova K., 2019, BERT: Pre-training of deep bidirectional transformers for language understanding, In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: human language technologies, volume 1, pp.4171-4186. https://doi.org/10.18653/v1/N19-1423 Esteva A., Robicquet A., Ramsundar B., Kuleshov V., DePristo M., Chou K., Cui C., Corrado G., Thrun S., and Dean J., 2019, A guide to deep learning in healthcare, Nature Medicine, 25(1): 24-29. https://doi.org/10.1038/s41591-018-0316-z Gu Y., Tinn R., Cheng H., Lucas M., Usuyama N., Liu X., Naumann T., Gao J., and Poon H., 2021, Domain-specific language model pretraining for biomedical natural language processing, ACM Transactions on Computing for Healthcare, 3(1): 1-23. https://doi.org/10.1145/3458754 Gururangan S., Marasović A., Swayamdipta S., Lo K., Beltagy I., Dauphin Y.N., and Smith N.A., 2020, Don’t stop pretraining: adapt language models to domains and tasks, Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp.8342-8360. https://doi.org/10.18653/v1/2020.acl-main.740
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