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Foundation large language models (LLMs) have shown an impressive ability to solve tasks across a wide range of fields including health.
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He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 770–778 (2016)
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Yang, X., Chen, A., PourNejatian, N., Shin, H.C., Smith, K.E., Parisien, C., Compas, C., Martin, C., Costa, A.B., Flores, M.G., et al.: A large language model for electronic health records. npj Digital Medicine 5
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2022
Later among the works it cites.
2022
Later among the works it cites.
Cosentino, J., Behsaz, B., Alipanahi, B., McCaw, Z.R., Hill, D., Schwantes-An, T.H., Lai, D., Carroll, A., Hobbs, B.D., Cho, M.H., et al.: Inference of chronic obstructive pulmonary disease with deep learning on raw spirograms identifies new genetic loci and improves risk models. Nature Genetics 55
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Alayrac, J.B., Donahue, J., Luc, P., Miech, A., Barr, I., Hasson, Y., Lenc, K., Mensch, A., Millican, K., Reynolds, M., et al.: Flamingo: a visual language model for few-shot learning. Advances in Neural Information Processing Systems 35
2022
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2022
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Dinh, T., Zeng, Y., Zhang, R., Lin, Z., Gira, M., Rajput, S., Sohn, J.y., Papailiopoulos, D., Lee, K.: Lift: Language-interfaced fine-tuning for non-language machine learning tasks. Advances in Neural Information Processing Systems 35
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Cited alongside, same era.
Kline, A., Wang, H., Li, Y., Dennis, S., Hutch, M., Xu, Z., Wang, F., Cheng, F., Luo, Y.: Multimodal machine learning in precision health: A scoping review. npj Digital Medicine 5
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Google: PaLM 2 technical report. arXiv preprint arXiv:2305.10403 (2023)
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Hegselmann, S., Buendia, A., Lang, H., Agrawal, M., Jiang, X., Sontag, D.: TabLLM: few-shot classification of tabular data with large language models. In: International Conference on Artificial Intelligence and Statistics. pp. 5549–5581. PMLR (2023)
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