Large Language Model as a Universal Clinical Multi-task Decoder
This provides a unified solution for handling a wide array of clinical tasks, addressing a significant bottleneck in clinical systems, though it is incremental in leveraging existing language models.
The paper tackles the challenge of managing diverse and emerging clinical tasks by proposing a novel paradigm that uses a pre-trained large language model as a universal clinical multi-task decoder, demonstrating robustness across hundreds of tasks with performance on par with traditional methods and exceptional adaptability in zero-shot and few-shot scenarios.
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.