Summary of Aqulia-med Llm: Pioneering Full-process Open-source Medical Language Models, by Lulu Zhao et al.
Aqulia-Med LLM: Pioneering Full-Process Open-Source Medical Language Models
by Lulu Zhao, Weihao Zeng, Xiaofeng Shi, Hua Zhou, Donglin Hao, Yonghua Lin
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 A bilingual medical language model, Aquila-Med, is proposed to address the challenge of outperforming humans in specific professional fields like medicine. The model leverages continue pre-training, supervised fine-tuning (SFT), and reinforcement learning from human feedback (RLHF) to improve performance. A large-scale Chinese and English medical dataset is constructed for continue pre-training, along with a high-quality SFT dataset covering extensive medical specialties. Additionally, the Direct Preference Optimization (DPO) dataset is developed for further alignment. Aquila-Med achieves notable results in single-turn, multi-turn dialogues, and medical multiple-choice questions, demonstrating its effectiveness. The models and datasets are open-sourced to contribute valuable resources to the research community. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Aquila-Med is a new language model that helps doctors and other medical professionals by understanding medical texts and conversations better. It’s like a super smart doctor’s assistant! To make it even smarter, the team trained Aquila-Med using lots of medical text data and had human experts help fine-tune its performance. The result is a model that can understand and answer complex medical questions accurately. This is important because medicine is a very specific field that requires deep knowledge. By open-sourcing the model and its training data, the team hopes to help other researchers improve their own language models for medical applications. |
Keywords
» Artificial intelligence » Alignment » Fine tuning » Language model » Optimization » Reinforcement learning from human feedback » Rlhf » Supervised
