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MedCare: Advancing Medical LLMs through Decoupling Clinical Alignment and Knowledge Aggregation by Liao, Yusheng; Jiang, Shuyang; Chen, Zhe; Wang, Yanfeng; Wang, Yu is a scholarly article available to read on EtoBox.

What is MedCare: Advancing Medical LLMs through Decoupling Clinical Alignment and Knowledge Aggregation about?

Large language models (LLMs) have shown substantial progress in natural language understanding and generation, proving valuable especially in the medical field. Despite advancements, challenges persist due to the complexity and diversity inherent in medical tasks, which can be categorized as knowledge-intensive tasks and alignment-required tasks. Previous approaches either ignore the latter task or focus on a minority of tasks and hence lose generalization. To address these drawbacks, we propose a progressive fine-tuning pipeline. This pipeline employs a Knowledge Aggregator and a Noise aggregator to encode diverse knowledge in the first stage and filter out detrimental information. In the second stage, we drop the Noise Aggregator to avoid the interference of suboptimal representation and leverage an additional alignment module optimized towards an orthogonal direction to the knowledge space to mitigate knowledge forgetting. Based on this two-stage paradigm, we proposed a Medical LLM through decoupling Clinical Alignment and Knowledge Aggregation (MedCare), which is designed to achieve state-of-the-art (SOTA) performance on over 20 medical tasks, as well as SOTA results on specifi

Author

Liao, Yusheng; Jiang, Shuyang; Chen, Zhe; Wang, Yanfeng; Wang, Yu

Published

2024

Language

EN