Summary of Sa-mdkif: a Scalable and Adaptable Medical Domain Knowledge Injection Framework For Large Language Models, by Tianhan Xu et al.
SA-MDKIF: A Scalable and Adaptable Medical Domain Knowledge Injection Framework for Large Language Models
by Tianhan Xu, Zhe Hu, Ling Chen, Bin Li
First submitted to arxiv on: 1 Feb 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 Recent advances in large language models (LLMs) have achieved impressive results in natural language processing (NLP) tasks. However, their effective application in the medical domain is hindered by a lack of medical domain knowledge. To address this limitation, researchers proposed SA-MDKIF, a scalable and adaptable framework that injects medical knowledge into general-purpose LLMs through instruction tuning. This framework consists of two stages: skill training and skill adaptation. In the first stage, 12 basic medical skills are defined and trained using AdaLoRA based on uniformly formatted instructional datasets. The second stage involves training the skill router using task-specific downstream data to integrate acquired skills with LLMs during inference. Experimental results on 9 different medical tasks demonstrate that SA-MDKIF improves performance by 10-20% compared to original LLMs, particularly for unseen medical tasks, showing an improvement of up to 30%. This framework has the potential to significantly improve NLP performance in the medical domain. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper is about making computers better at understanding and working with medical information. Currently, these computers are very good at understanding general language, but struggle when it comes to medical topics. The scientists created a new way to teach these computers medical skills, which they call SA-MDKIF. This method involves teaching the computer 12 basic medical skills and then using those skills to help the computer understand more complex medical information. The results show that this method can improve the computer’s ability to perform tasks related to medicine by 10-20%. This is an important step towards making computers more helpful in the medical field. |
Keywords
» Artificial intelligence » Inference » Instruction tuning » Natural language processing » Nlp
