Large Language Model as a Universal Clinical Multi-task Decoder

TL;DR

This paper introduces a universal clinical multi-task decoder using a pre-trained large language model, enabling efficient handling of diverse and emerging clinical tasks with high adaptability and zero-shot capabilities.

Contribution

It proposes a novel paradigm that employs a large language model as a flexible, multi-task decoder for clinical applications, simplifying task addition and improving adaptability.

Findings

Performs on par with traditional methods across hundreds of tasks.

Exhibits strong zero-shot performance on new tasks.

Shows superior data efficiency in few-shot learning scenarios.

Abstract

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.