Data Sheet 2_评估Deepseek V3、Doubao和Kimi1.5回答慢性非细菌性骨髓炎相关知识查询的准确性与可重复性

数据集获取（Figshare）

• 源站标题：Data Sheet 2_Evaluation of the accuracy and repeatability of Deepseek V3, Doubao, and Kimi1.5 in answering knowledge-related queries about chronic non-bacterial osteitis.zip
• DOI：10.3389/frai.2025.1629149.s002
• 发布日期：2025-09-29
• 文件数：1 个
• 文件总体积：80.75 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Data_Sheet_2_Evaluation_of_the_accuracy_and_repeatability_of_Deepseek_V3_Doubao_and_Kimi1_5_in_answering_knowledge-related_queries_about_chronic_non-bacterial_osteitis_zip/30230389

命令行下载

curl -L -o "Data Sheet 2_Evaluation of the accuracy and repeatability of Deepseek V3, Doubao, and Kimi1.5 in answering knowledge-related queries about chronic non.zip" "https://ndownloader.figshare.com/files/58342195"

文件清单

• Data Sheet 2_Evaluation of the accuracy and repeatability of Deepseek V3, Doubao, and Kimi1.5 in answering knowledge-related queries about chronic non.zip（80.75 KB）

文件总体积 80.75 KB（源站 API 实测）。

数据加载示例（图像类）

from PIL import Image
import glob, os

files = (glob.glob(os.path.join(path, "**", "*.png"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.jpg"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.tif"), recursive=True))
print("图像文件数:", len(files))
img = Image.open(files[0]); print("尺寸/模式:", img.size, img.mode)

# torchvision Dataset 方式:
# from torchvision import datasets
# ds = datasets.ImageFolder(path)  # 要求 子目录=类别

目录组织与标注格式以源站说明和下载后实际文件为准。

数据说明

BackgroundThere are significant differences in the diagnosis and treatment of chronic non-bacterial osteitis (CNO), and there is an urgent need for health education efforts to enhance awareness of this condition. Deepseek V3, Doubao, and Kimi1.5 are highly popular language models in China that can provide knowledge related to diseases. This article aims to investigate the accuracy and reproducibility of the responses provided by these three artificial intelligence (AI) language models in answering questions about CNO.
MethodsAccording to the latest expert consensus, 16 questions related to CNO were collected. The three AI language models were separately asked these questions at three different times. The answers were independently evaluated by two orthopedic experts.
ResultsAmong the responses of the three AI models to 16 CNO-related questions across three rounds of testing, only Doubao received “Completely incorrect” ratings (accounting for 6.25%) in the third round of scoring by Reviewer 2. During the answering process, Doubao had the shortest response time and provided the most words in its answers. In the first and third rounds of scoring by the first expert, Kimi scored the highest (3.938 ± 0.342, 3.875 ± 0.873), while in the second round, Doubao scored the highest (3.875 ± 0.5). In the second round of scoring by the second expert, Doubao received the highest score (3.812 ± 0.403). In the first and third rounds, Kimi1.5 received the highest score (3.812 ± 0.602, 3.812 ± 0.704).
ConclusionDeepseek V3, Doubao, and Kimi1.5 are capable of answering most questions related to CNO with good accuracy and reproducibility, showing no significant differences.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/frai.2025.1629149.s002（source: https://frontiersin.figshare.com/articles/dataset/Data_Sheet_2_Evaluation_of_the_accuracy_and_repeatability_of_Deepseek_V3_Doubao_and_Kimi1_5_in_answering_knowledge-related_queries_about_chronic_non-bacterial_osteitis_zip/30230389）。