Summary of Fine-tuning Medical Language Models For Enhanced Long-contextual Understanding and Domain Expertise, by Qimin Yang et al.
Fine-Tuning Medical Language Models for Enhanced Long-Contextual Understanding and Domain Expertise
by Qimin Yang, Rongsheng Wang, Jiexin Chen, Runqi Su, Tao Tan
First submitted to arxiv on: 16 Jul 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 The study investigates the phenomenon of reduced performance in understanding long-context in medical Large Language Models (LLMs). By fine-tuning these models using domain-specific question and answer datasets, their professional domain knowledge and Q&A abilities have improved significantly. However, despite improvements in specific domain knowledge, medical LLMs’ performance in long-context understanding has declined, especially compared to general language models with similar parameters. The authors designed experiments to conduct open-book professional knowledge exams on all models to evaluate their ability to read long-context. By adjusting the proportion and quantity of general data and medical data during fine-tuning, they aimed to determine the best data composition to optimize the professional model while achieving a balance between long-context performance and specific domain knowledge. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The study looks at how well large language models do when answering questions about medicine. These models are good at understanding short passages of text but struggle with longer ones. The researchers tested different ways of training these models and found that by using more general information, they can improve their ability to understand long texts while still keeping their medical knowledge. |
Keywords
» Artificial intelligence » Fine tuning
