ThaiLLM/med-app-instruct
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---
dataset_info:
features:
- name: conversations
dtype: string
- name: tool_name
dtype: string
splits:
- name: qwen3_5_27b
num_bytes: 2420420878
num_examples: 376439
- name: qwen3_5_plus
num_bytes: 2150192481
num_examples: 376477
- name: claude_4_6_sonnet
num_bytes: 2147726822
num_examples: 376477
download_size: 5369403541
dataset_size: 8942688074
configs:
- config_name: default
data_files:
- split: qwen3_5_27b
path: data/qwen3_5_27b-*
- split: qwen3_5_plus
path: data/qwen3_5_plus-*
- split: claude_4_6_sonnet
path: data/claude_4_6_sonnet-*
license: mit
task_categories:
- text-generation
language:
- th
- en
pretty_name: med-app-instruct
size_categories:
- 100K<n<1M
---
# ThaiLLM Medical Instruction with Tool Calling
A synthetic Thai medical instruction-following dataset with tool calling capabilities, designed for training language models to handle healthcare-related queries through a mobile health assistant interface.
## Dataset Description
This dataset contains multi-turn conversations between users and an AI health assistant, featuring both direct responses and tool-augmented interactions. The conversations simulate a realistic Thai healthcare application scenario where the assistant can invoke various medical tools to provide accurate, contextual assistance.
### Dataset Structure
Each example follows the OpenAI chat completion format and is compatible with Hugging Face's [SFTTrainer](https://huggingface.co/docs/trl/en/sft_trainer) for fine-tuning.
```python
{
"conversations": [
{"role": "system", "content": "...system prompt with tool definitions..."},
{"role": "user", "content": "...user query in Thai..."},
{"role": "assistant", "tool_calls": [...]}, # Tool invocation
{"role": "tool", "name": "tool_name", "content": "...tool results..."},
{"role": "assistant", "content": "...final response..."}
],
"tool_name": "..." # The primary tool used in this conversation
}
```
### Data Splits
| Split | Description | Response Mining Model |
|:------|:------------|:----------------------|
| `qwen3_5_27b` | Responses mined from Qwen3-235B-A22B (27B active params) | [Qwen/Qwen3-235B-A22B](https://huggingface.co/Qwen/Qwen3-235B-A22B) |
| `qwen3_5_plus` | Responses mined from Qwen3.5-Plus | Qwen3.5-Plus (via OpenRouter) |
| `claude_4_6_sonnet` | Responses mined from Claude Sonnet 4.6 | [Claude Sonnet 4.6](https://docs.anthropic.com/en/docs/about-claude/models) |
## Tools
The dataset includes interactions with 7 healthcare-related tools:
| Tool Name | Description | Response Format |
|:----------|:------------|:----------------|
| `search_medical_facts` | Retrieves relevant medical facts from a knowledge base to answer health-related questions | Structured response with `<response>` and `<reference>` tags containing citations |
| `prescreen` | Initiates a symptom severity assessment pipeline with differential diagnosis | Recommendation based on severity classification |
| `get_health_emergency_contact` | Returns Thailand emergency health hotlines (ambulance, poison control, mental health) | List of relevant emergency contacts |
| `create_appointment` | Creates a new appointment with a hospital/clinic | Confirmation of appointment details |
| `create_reminder` | Creates a medication reminder | Confirmation of reminder setup |
| `list_appointment` | Retrieves and allows interaction with existing appointments | List of appointments or confirmation of edits |
| `list_reminder` | Retrieves and allows interaction with existing medication reminders | List of reminders or confirmation of edits |
### Tool Categories
- **Informational Queries (IQ):** `search_medical_facts` - Medical RAG with citation requirements
- **Health Assessment:** `prescreen` - Symptom severity classification
- **Emergency Services:** `get_health_emergency_contact` - Thailand-specific emergency hotlines
- **Scheduling & Management:** `create_appointment`, `create_reminder`, `list_appointment`, `list_reminder`
## Data Generation Pipeline
### Source Data
The dataset is constructed from multiple sources:
1. **Medical Facts:** Retrieved from [ThaiLLM/med-facts](https://huggingface.co/datasets/ThaiLLM/med-facts) and [ThaiLLM/med-articles](https://huggingface.co/datasets/ThaiLLM/med-articles)
2. **Medical Q&A:** Based on [ThaiLLM/med-qas-synthetic](https://huggingface.co/datasets/ThaiLLM/med-qas-synthetic) (refined baseline split)
3. **Synthetic Tool Queries:** Generated for appointment, reminder, prescreen, and emergency contact scenarios
4. **Negative Samples:** Sourced from [kunato/typhoon-s-instruct-post-training](https://huggingface.co/datasets/kunato/typhoon-s-instruct-post-training) for non-tool conversations
### Generation Process
1. **Query Synthesis:** User queries are synthetically generated based on predefined scenarios covering various medical and scheduling use cases
2. **Tool Mocking:** Tool responses are simulated with realistic data (appointments, reminders, medical facts, prescreen results)
3. **Response Mining:** Final assistant responses are mined from a large language model given the full conversation context
4. **Format Conversion:** Conversations are converted to SFTTrainer-compatible format
## Intended Use
### Primary Use Cases
- Fine-tuning LLMs for Thai medical chatbot applications
- Training models to properly invoke and respond to tool calls
- Building healthcare virtual assistants with scheduling capabilities
- Research on medical information retrieval with citations
### Out-of-Scope Use
- This dataset should **NOT** be used for actual medical diagnosis
- Not suitable for providing real medical advice without human oversight
- The emergency contact information is specific to Thailand and may not apply to other regions
## Dataset Statistics
| Split | Samples |
|:------|--------:|
| `qwen3_5_27b` | 376,439 |
| `qwen3_5_plus` | 376,477 |
| `claude_4_6_sonnet` | TBD |
### Distribution by Tool (per split, approximate)
| Tool Name | Samples | Percentage |
|:----------|--------:|-----------:|
| `negatives` (no tool call) | 357,072 | 94.85% |
| `search_medical_facts` | 14,126 | 3.75% |
| `get_health_emergency_contact` | 1,106 | 0.29% |
| `create_appointment` | 1,000 | 0.27% |
| `create_reminder` | 1,000 | 0.27% |
| `list_reminder` | 778 | 0.21% |
| `list_appointment` | 773 | 0.21% |
| `prescreen` | 622 | 0.17% |
## Limitations and Biases
1. **Synthetic Nature:** Responses are generated by LLMs and may contain hallucinations or inaccuracies
2. **Thailand-Specific:** Emergency contacts and some medical practices are specific to Thailand's healthcare system
3. **Language Bias:** Primarily designed for Thai language; English support is secondary
4. **Medical Disclaimer:** This is synthetic training data and should not be used for actual medical decisions
5. **Tool Simulation:** Tool outputs are mocked/simulated and do not represent real medical data
## Related Datasets
- [ThaiLLM/med-articles](https://huggingface.co/datasets/ThaiLLM/med-articles) - Source medical articles
- [ThaiLLM/med-facts](https://huggingface.co/datasets/ThaiLLM/med-facts) - Extracted medical facts
- [ThaiLLM/med-qas-synthetic](https://huggingface.co/datasets/ThaiLLM/med-qas-synthetic) - Medical Q&A pairs
- [ThaiLLM/med-qas-golden-articles](https://huggingface.co/datasets/ThaiLLM/med-qas-golden-articles) - Human-annotated gold-label data
---
dataset_info:
features:
- name: conversations
dtype: string
- name: tool_name
dtype: string
splits:
- name: qwen3_5_27b
num_bytes: 2420420878
num_examples: 376439
- name: qwen3_5_plus
num_bytes: 2150192481
num_examples: 376477
- name: claude_4_6_sonnet
num_bytes: 2147726822
num_examples: 376477
download_size: 5369403541
dataset_size: 8942688074
configs:
- config_name: default
data_files:
- split: qwen3_5_27b
path: data/qwen3_5_27b-*
- split: qwen3_5_plus
path: data/qwen3_5_plus-*
- split: claude_4_6_sonnet
path: data/claude_4_6_sonnet-*
license: mit
task_categories:
- text-generation
language:
- th
- en
pretty_name: med-app-instruct
size_categories:
- 100K<n<1M
---
# 带工具调用的ThaiLLM医疗指令数据集
本数据集为合成式泰语医疗指令遵循数据集，支持工具调用功能，旨在训练大语言模型通过移动健康助手界面处理医疗相关查询。
## 数据集说明
本数据集包含用户与AI健康助手的多轮对话，涵盖直接回复与工具增强交互两种形式。对话模拟了真实的泰语医疗应用场景，助手可调用各类医疗工具以提供精准且贴合上下文的协助。
### 数据集结构
每条样本均遵循OpenAI对话补全格式，可兼容Hugging Face的[SFTTrainer](https://huggingface.co/docs/trl/en/sft_trainer)用于微调训练。
python
{
"conversations": [
{"role": "system", "content": "...带工具定义的系统提示词..."},
{"role": "user", "content": "...泰语用户查询..."},
{"role": "assistant", "tool_calls": [...]}, # 工具调用
{"role": "tool", "name": "tool_name", "content": "...工具返回结果..."},
{"role": "assistant", "content": "...最终回复..."}
],
"tool_name": "..." # 本次对话中使用的主要工具
}
### 数据划分集
| 划分名称 | 说明 | 回复挖掘模型 |
|:------|:------------|:----------------------|
| `qwen3_5_27b` | 从Qwen3-235B-A22B（270亿活跃参数）中挖掘得到的回复 | [Qwen/Qwen3-235B-A22B](https://huggingface.co/Qwen/Qwen3-235B-A22B) |
| `qwen3_5_plus` | 从Qwen3.5-Plus中挖掘得到的回复 | Qwen3.5-Plus（通过OpenRouter调用） |
| `claude_4_6_sonnet` | 从Claude Sonnet 4.6中挖掘得到的回复 | [Claude Sonnet 4.6](https://docs.anthropic.com/en/docs/about-claude/models) |
## 工具集
本数据集包含7类医疗相关工具的交互场景：
| 工具名称 | 功能说明 | 回复格式 |
|:----------|:------------|:----------------|
| `search_medical_facts` | 从知识库中检索相关医疗事实以解答健康相关问题 | 采用`<response>`和`<reference>`标签的结构化回复，包含引用信息 |
| `prescreen` | 启动症状严重程度评估流水线并生成鉴别诊断 | 基于严重程度分类输出建议 |
| `get_health_emergency_contact` | 返回泰国境内的医疗紧急热线（救护车、毒物控制、心理健康热线） | 相关紧急联系方式列表 |
| `create_appointment` | 创建医院或诊所的新预约 | 预约详情确认信息 |
| `create_reminder` | 创建用药提醒 | 提醒设置确认信息 |
| `list_appointment` | 检索并支持对现有预约的交互操作 | 预约列表或编辑确认信息 |
| `list_reminder` | 检索并支持对现有用药提醒的交互操作 | 提醒列表或编辑确认信息 |
### 工具分类
- **信息查询类（IQ）**：`search_medical_facts` —— 带引用要求的医疗检索增强生成（RAG）
- **健康评估类**：`prescreen` —— 症状严重程度分类
- **紧急服务类**：`get_health_emergency_contact` —— 泰国专属紧急热线查询
- **日程与管理类**：`create_appointment`、`create_reminder`、`list_appointment`、`list_reminder`
## 数据生成流水线
### 源数据
本数据集由多源数据构建：
1. **医疗事实**：源自[ThaiLLM/med-facts](https://huggingface.co/datasets/ThaiLLM/med-facts)与[ThaiLLM/med-articles](https://huggingface.co/datasets/ThaiLLM/med-articles)
2. **医疗问答**：基于[ThaiLLM/med-qas-synthetic](https://huggingface.co/datasets/ThaiLLM/med-qas-synthetic)（精炼后的基准划分集）
3. **合成工具查询**：针对预约、提醒、预筛查与紧急联系场景生成的工具查询
4. **负样本**：源自[kunato/typhoon-s-instruct-post-training](https://huggingface.co/datasets/kunato/typhoon-s-instruct-post-training)，用于非工具调用的对话
### 生成流程
1. **查询合成**：基于覆盖各类医疗与日程使用场景的预定义场景，合成用户查询
2. **工具模拟**：用真实数据（预约、提醒、医疗事实、预筛查结果）模拟工具回复
3. **回复挖掘**：基于完整对话上下文，从大语言模型中挖掘最终助手回复
4. **格式转换**：将对话转换为SFTTrainer兼容格式
## 预期用途
### 主要使用场景
- 针对泰语医疗聊天机器人应用的大语言模型微调
- 训练模型正确调用工具并生成对应回复
- 构建带日程管理功能的医疗虚拟助手
- 开展带引用的医疗信息检索相关研究
### 禁止使用场景
- 本数据集**不得**用于实际医疗诊断
- 未经人类监督的情况下，不适用于提供真实医疗建议
- 紧急联系信息仅针对泰国境内，不适用于其他地区
## 数据集统计信息
| 划分名称 | 样本量 |
|:------|--------:|
| `qwen3_5_27b` | 376,439 |
| `qwen3_5_plus` | 376,477 |
| `claude_4_6_sonnet` | 待公布（TBD） |
### 各划分集工具分布（近似值）
| 工具名称 | 样本量 | 占比 |
|:----------|--------:|-----------:|
| `negatives`（无工具调用） | 357,072 | 94.85% |
| `search_medical_facts` | 14,126 | 3.75% |
| `get_health_emergency_contact` | 1,106 | 0.29% |
| `create_appointment` | 1,000 | 0.27% |
| `create_reminder` | 1,000 | 0.27% |
| `list_reminder` | 778 | 0.21% |
| `list_appointment` | 773 | 0.21% |
| `prescreen` | 622 | 0.17% |
## 局限性与偏差
1. **合成特性**：回复由大语言模型生成，可能包含幻觉内容或不准确信息
2. **泰国专属特性**：紧急联系信息与部分医疗实践仅适配泰国医疗体系
3. **语言偏向**：主要面向泰语设计，英语支持为次要适配
4. **医疗免责声明**：本数据集为合成训练数据，不得用于实际医疗决策
5. **工具模拟**：工具输出为模拟生成，不代表真实医疗数据
## 相关数据集
- [ThaiLLM/med-articles](https://huggingface.co/datasets/ThaiLLM/med-articles) —— 源医疗文章数据集
- [ThaiLLM/med-facts](https://huggingface.co/datasets/ThaiLLM/med-facts) —— 提取的医疗事实数据集
- [ThaiLLM/med-qas-synthetic](https://huggingface.co/datasets/ThaiLLM/med-qas-synthetic) —— 合成医疗问答对数据集
- [ThaiLLM/med-qas-golden-articles](https://huggingface.co/datasets/ThaiLLM/med-qas-golden-articles) —— 人工标注的金标准数据集
提供机构：
ThaiLLM
