PediatricsGPT: Large Language Models as Chinese Medical Assistants for Pediatric Applications
Authors: Dingkang Yang, Jinjie Wei, Dongling Xiao, Shunli Wang, Tong Wu, Gang Li, Mingcheng Li, Shuaibing Wang, Jiawei Chen, Yue Jiang, Qingyao Xu, Ke Li, Peng Zhai, Lihua Zhang
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive results based on the metrics, GPT-4, and doctor evaluations on distinct downstream tasks show that Pediatrics GPT consistently outperforms previous Chinese medical LLMs. |
| Researcher Affiliation | Collaboration | 1Academy for Engineering and Technology, Fudan University, Shanghai, China 2Tencent Youtu Lab, Shanghai, China |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | Yes | The project and data will be released at https://github.com/ydk122024/Pediatrics GPT. |
| Open Datasets | Yes | Motivated by these observations, we construct Ped Corpus, a high-quality dataset with over 300,000 instructions across single-turn and multi-turn medical conversations. Besides containing generalist healthcare data, Ped Corpus incorporates multi-dimensional corpora from pediatric textbooks, guidelines, and knowledge graphs to ensure medical knowledge s accuracy. |
| Dataset Splits | Yes | We specify eval_steps at 100 and save the best-performing weights on the validation set to ensure optimal results. |
| Hardware Specification | Yes | The model training is accomplished through the Py Torch platform with Accelerate and Deep Speed packages using eight Nvidia A800 GPUs. |
| Software Dependencies | No | The paper mentions using 'Py Torch platform with Accelerate and Deep Speed packages' but does not specify version numbers for any of these software components. |
| Experiment Setup | Yes | More detailed hyper-parameter configurations for different stages are shown in Appendix C. |