Summary of Large Language Model As a Universal Clinical Multi-task Decoder, by Yujiang Wu et al.
Large Language Model as a Universal Clinical Multi-task Decoder
by Yujiang Wu, Hongjian Song, Jiawen Zhang, Xumeng Wen, Shun Zheng, Jiang Bian
First submitted to arxiv on: 18 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a novel machine learning paradigm that leverages large language models as universal decoders for clinical systems. The framework enables efficient handling of diversified clinical tasks, including adapting to new tasks with minimal additional instruction templates. Through extensive validation across hundreds of tasks, the approach demonstrates robust multi-task predictions, comparable performance to traditional methods, and exceptional adaptability in few-shot scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to use big language models to help medical systems get better at many different clinical tasks. It’s like having a super smart doctor who can learn from all sorts of medical problems and then help solve them more easily. The approach is really good at learning many things at once, which is helpful for medical systems that have to deal with lots of different situations. |
Keywords
» Artificial intelligence » Few shot » Machine learning » Multi task
