---
language:
- kk
license: cc-by-nc-4.0
task_categories:
- text-generation
- question-answering
tags:
- kazakh
- agentic-ai
- tool-use
- function-calling
- final-answer-synthesis
- multi-tool-use
- calendar-agent
- email-agent
- task-automation
- llm-agents
pretty_name: Kazakh Final Answer Synthesis and Multi-Tool Task Automation Dataset
size_categories:
- 1K<n<10K
---

# 🇰🇿 Kazakh Final Answer Synthesis and Multi-Tool Task Automation Dataset

## Dataset Summary

**Kazakh Final Answer Synthesis and Multi-Tool Task Automation Dataset** is a Kazakh-language dataset designed for training and evaluating Large Language Models (LLMs) in agentic AI workflows that require multi-tool execution and final answer synthesis.

The dataset contains user requests, available tool schemas, expected tool calls, simulated tool outputs, and full multi-turn interaction traces. It focuses on scenarios where an assistant must complete a practical task through multiple tools, combine the results, and produce a clear final answer for the user.


---

## 📊 Dataset Statistics

### General Metrics

| Metric | Count |
| :--- | :--- |
| **Total Samples** | 2,021 |
| **Total Words** (approx.) | 616,889 |
| **Avg. Words per Sample** | 305 |

### Word Count Distribution Per Field

The following table details the distribution of word counts across different fields in the dataset.

| Field | Mean | Median | Min | Max | Total Words |
| :--- | :--- | :--- | :--- | :--- | :--- |
| **answers** | 29.9 | 26.0 | 6 | 147 | 60,469 |
| **category** | 1.0 | 1.0 | 1 | 1 | 2,021 |
| **difficulty** | 1.0 | 1.0 | 1 | 1 | 2,021 |
| **id** | 1.0 | 1.0 | 1 | 1 | 2,021 |
| **query** | 16.1 | 15.0 | 3 | 39 | 32,495 |
| **tools** | 86.5 | 82.0 | 24 | 160 | 174,856 |
| **turns** | 169.7 | 162.0 | 73 | 381 | 343,006 |


![image](https://cdn-uploads.huggingface.co/production/uploads/64f75f7bd04a890f5347d436/BJKqTckIpkTch8_0JhgNp.png)

---

## Dataset Structure

Each dataset instance represents a complete multi-tool task automation workflow. A sample usually includes a Kazakh user query, available tool schemas, expected tool calls, mock tool outputs, and a final synthesized assistant response.

### Data Fields

- **`id`**: A unique identifier for the sample.

- **`query`**: The original user request in Kazakh. The request often asks the assistant to complete a practical workflow, such as creating an event and notifying another person.

- **`category`**: The task category. For example, `09_final_answer_synthesis` indicates that the sample focuses on combining tool results into a final user-facing answer.

- **`tools`**: A list of tools available to the assistant. Each tool contains:
  - `name`: the tool name;
  - `description`: the tool’s purpose;
  - `parameters`: the expected argument schema;
  - `mock_response`: the expected response format.

- **`difficulty`**: The difficulty level of the sample.

- **`answers`**: The expected sequence of tool calls. This field may include multiple tools that must be called in the correct order.

- **`turns`**: The complete interaction trace, including:
  - user request;
  - assistant planning;
  - tool calls;
  - mock tool outputs;
  - final synthesized answer.

---

## Data Instance

Below is one representative example from the dataset.

```json
"id": "aziz_09_final_answer_synthesis_1",
        "query": "Ертең сағат 15:00-де \"Клиентпен кездесу\" деп күнтізбеге іс-шара қосып, оның сілтемесін manager@example.com поштасына жібер.",
        "category": "09_final_answer_synthesis",
        "tools": [
            {
                "name": "calendar.add",
                "description": "Add new calendar event",
                "parameters": {
                    "title": {
                        "type": "string",
                        "description": "Event title",
                        "required": true
                    },
                    "datetime": {
                        "type": "string",
                        "description": "Start time RFC3339",
                        "required": true
                    },
                    "duration": {
                        "type": "int",
                        "description": "Duration in minutes",
                        "required": false
                    },
                    "location": {
                        "type": "string",
                        "description": "Event location",
                        "required": false
                    }
                },
                "mock_response": {
                    "status": "",
                    "event_id": "",
                    "link": ""
                }
            },
            {
                "name": "email.send",
                "description": "Send email message",
                "parameters": {
                    "to": {
                        "type": "string",
                        "description": "Recipient email",
                        "required": true
                    },
                    "subject": {
                        "type": "string",
                        "description": "Email subject",
                        "required": true
                    },
                    "body": {
                        "type": "string",
                        "description": "Email content",
                        "required": true
                    }
                },
                "mock_response": {
                    "status": "",
                    "message_id": "",
                    "timestamp": ""
                }
            }
        ],
        "difficulty": "hard",
        "answers": [
            {
                "name": "calendar.add",
                "arguments": "{\"title\": \"Клиентпен кездесу\", \"datetime\": \"2024-06-22T15:00:00+06:00\", \"duration\": 60, \"location\": \"Офис, 3-қабат\"}"
            },
            {
                "name": "email.send",
                "arguments": "{\"to\": \"manager@example.com\", \"subject\": \"Клиентпен кездесу іс-шарасы\", \"body\": \"Құрметті менеджер, жаңа кездесу күнтізбеге қосылды: https://calendar.example.com/event/evt_12345\"}"
            }
        ],
        "turns": [
            {
                "role": "user",
                "content": "Ертең сағат 15:00-де \"Клиентпен кездесу\" деп күнтізбеге іс-шара қосып, оның сілтемесін manager@example.com поштасына жібер."
            },
            {
                "role": "assistant",
                "content": "Алдымен іс-шараны күнтізбеге қосып, кейін сілтемесін электронды пошта арқылы жіберемін.",
                "meta": {
                    "plan": "calendar.add + email.send, then merge data",
                    "tags": [],
                    "difficulty": "hard"
                }
            },
            {
                "role": "assistant",
                "content": "",
                "tool_call": {
                    "name": "calendar.add",
                    "arguments": "{\"title\": \"Клиентпен кездесу\", \"datetime\": \"2024-06-22T15:00:00+06:00\", \"duration\": 60, \"location\": \"Офис, 3-қабат\"}"
                }
            },
            {
                "role": "tool",
                "content": "{\"status\": \"success\", \"event_id\": \"evt_12345\", \"link\": \"https://calendar.example.com/event/evt_12345\"}"
            },
            {
                "role": "assistant",
                "content": "",
                "tool_call": {
                    "name": "email.send",
                    "arguments": "{\"to\": \"manager@example.com\", \"subject\": \"Клиентпен кездесу іс-шарасы\", \"body\": \"Құрметті менеджер, жаңа кездесу күнтізбеге қосылды: https://calendar.example.com/event/evt_12345\"}"
                }
            },
            {
                "role": "tool",
                "content": "{\"status\": \"sent\", \"message_id\": \"msg_67890\", \"timestamp\": \"2024-06-21T10:05:00+06:00\"}"
            },
            {
                "role": "assistant",
                "content": "Күнтізбеге \"Клиентпен кездесу\" іс-шарасы қосылды және сілтемесі manager@example.com поштасына жіберілді. (Дерек көзі: calendar.add + email.send)"
            }
        ]
    }
```

## Funding

This dataset was developed as part of the project funded by the Ministry of Science and Higher Education of the Republic of Kazakhstan under Grant No. BR24993001, “Creation of a Large Language Model (LLM) to Support the Kazakh Language and Advance Technological Development.”

## Citation

If you use this dataset in your research, please cite the following article:

### APA

Kadyrbek, N., Tuimebayev, Z., Mansurova, M., & Viegas, V. (2025). The development of small-scale language models for low-resource languages, with a focus on Kazakh and direct preference optimization. *Big Data and Cognitive Computing, 9*(5), 137. [https://doi.org/10.3390/bdcc9050137](https://doi.org/10.3390/bdcc9050137)

### BibTeX

```bibtex
@article{kadyrbek2025development,
  title     = {The Development of Small-Scale Language Models for Low-Resource Languages, with a Focus on Kazakh and Direct Preference Optimization},
  author    = {Kadyrbek, Nurgali and Tuimebayev, Zhanseit and Mansurova, Madina and Viegas, Vitor},
  journal   = {Big Data and Cognitive Computing},
  volume    = {9},
  number    = {5},
  pages     = {137},
  year      = {2025},
  publisher = {MDPI},
  doi       = {10.3390/bdcc9050137},
  url       = {https://www.mdpi.com/2504-2289/9/5/137}
}
```