UltraMedical: Building Specialized Generalists in Biomedicine
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866929566046486528 |
|---|---|
| author | Zhang, Kaiyan Zeng, Sihang Hua, Ermo Ding, Ning Chen, Zhang-Ren Ma, Zhiyuan Li, Haoxin Cui, Ganqu Qi, Biqing Zhu, Xuekai Lv, Xingtai Jinfang, Hu Liu, Zhiyuan Zhou, Bowen |
| author_facet | Zhang, Kaiyan Zeng, Sihang Hua, Ermo Ding, Ning Chen, Zhang-Ren Ma, Zhiyuan Li, Haoxin Cui, Ganqu Qi, Biqing Zhu, Xuekai Lv, Xingtai Jinfang, Hu Liu, Zhiyuan Zhou, Bowen |
| contents | Large Language Models (LLMs) have demonstrated remarkable capabilities across various domains and are moving towards more specialized areas. Recent advanced proprietary models such as GPT-4 and Gemini have achieved significant advancements in biomedicine, which have also raised privacy and security challenges. The construction of specialized generalists hinges largely on high-quality datasets, enhanced by techniques like supervised fine-tuning and reinforcement learning from human or AI feedback, and direct preference optimization. However, these leading technologies (e.g., preference learning) are still significantly limited in the open source community due to the scarcity of specialized data. In this paper, we present the UltraMedical collections, which consist of high-quality manual and synthetic datasets in the biomedicine domain, featuring preference annotations across multiple advanced LLMs. By utilizing these datasets, we fine-tune a suite of specialized medical models based on Llama-3 series, demonstrating breathtaking capabilities across various medical benchmarks. Moreover, we develop powerful reward models skilled in biomedical and general reward benchmark, enhancing further online preference learning within the biomedical LLM community. Datasets and models are available at https://github.com/TsinghuaC3I/UltraMedical |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_03949 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | UltraMedical: Building Specialized Generalists in Biomedicine Zhang, Kaiyan Zeng, Sihang Hua, Ermo Ding, Ning Chen, Zhang-Ren Ma, Zhiyuan Li, Haoxin Cui, Ganqu Qi, Biqing Zhu, Xuekai Lv, Xingtai Jinfang, Hu Liu, Zhiyuan Zhou, Bowen Computation and Language Large Language Models (LLMs) have demonstrated remarkable capabilities across various domains and are moving towards more specialized areas. Recent advanced proprietary models such as GPT-4 and Gemini have achieved significant advancements in biomedicine, which have also raised privacy and security challenges. The construction of specialized generalists hinges largely on high-quality datasets, enhanced by techniques like supervised fine-tuning and reinforcement learning from human or AI feedback, and direct preference optimization. However, these leading technologies (e.g., preference learning) are still significantly limited in the open source community due to the scarcity of specialized data. In this paper, we present the UltraMedical collections, which consist of high-quality manual and synthetic datasets in the biomedicine domain, featuring preference annotations across multiple advanced LLMs. By utilizing these datasets, we fine-tune a suite of specialized medical models based on Llama-3 series, demonstrating breathtaking capabilities across various medical benchmarks. Moreover, we develop powerful reward models skilled in biomedical and general reward benchmark, enhancing further online preference learning within the biomedical LLM community. Datasets and models are available at https://github.com/TsinghuaC3I/UltraMedical |
| title | UltraMedical: Building Specialized Generalists in Biomedicine |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2406.03949 |