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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2406.12738 |
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| _version_ | 1866914839234871296 |
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| author | Wu, Yujiang Song, Hongjian Zhang, Jiawen Wen, Xumeng Zheng, Shun Bian, Jiang |
| author_facet | Wu, Yujiang Song, Hongjian Zhang, Jiawen Wen, Xumeng Zheng, Shun Bian, Jiang |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_12738 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Large Language Model as a Universal Clinical Multi-task Decoder Wu, Yujiang Song, Hongjian Zhang, Jiawen Wen, Xumeng Zheng, Shun Bian, Jiang Computation and Language Artificial Intelligence 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. |
| title | Large Language Model as a Universal Clinical Multi-task Decoder |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2406.12738 |