Large Language Model as a Universal Clinical Multi-task Decoder
<|reference_start|>Large Language Model as a Universal Clinical Multi-task Decoder: The development of effective machine learning methodologies for enhancing the efficiency and accuracy of clinical systems is crucial. Despite significant research efforts, managing a plethora of diversified clinical tasks and adapting to emerging new tasks remain significant challenges. This paper presents a novel paradigm that employs a pre-trained large language model as a universal clinical multi-task decoder. This approach leverages the flexibility and diversity of language expressions to handle task topic variations and associated arguments. The introduction of a new task simply requires the addition of a new instruction template. We validate this framework across hundreds of tasks, demonstrating its robustness in facilitating multi-task predictions, performing on par with traditional multi-task learning and single-task learning approaches. Moreover, it shows exceptional adaptability to new tasks, with impressive zero-shot performance in some instances and superior data efficiency in few-shot scenarios. This novel approach offers a unified solution to manage a wide array of new and emerging tasks in clinical applications.<|reference_end|>
@article{wu2024large,
    title={Large Language Model as a Universal Clinical Multi-task Decoder},
    author={Yujiang Wu, Hongjian Song, Jiawen Zhang, Xumeng Wen, Shun Zheng, Jiang
  Bian},
    journal={arXiv preprint arXiv:2406.12738},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.12738},
    primaryClass={cs.CL cs.AI}
}