Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates.
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 | Venue PDF | LLM Run Details | Input Tokens: 37,346 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,540 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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. |