تقرير
Large Language Model as a Universal Clinical Multi-task Decoder
| العنوان: | Large Language Model as a Universal Clinical Multi-task Decoder |
|---|---|
| المؤلفون: | Wu, Yujiang, Song, Hongjian, Zhang, Jiawen, Wen, Xumeng, Zheng, Shun, Bian, Jiang |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Computation and Language, Computer Science - Artificial Intelligence |
| الوصف: | 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. Comment: Work in progress |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2406.12738 |
| رقم الأكسشن: | edsarx.2406.12738 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |