---
language:
- kk
license: cc-by-nc-4.0
task_categories:
- text-generation
- question-answering
tags:
- kazakh
- agentic-ai
- tool-use
- function-calling
- tool-output-interpretation
- financial-assistant
- banking-tools
- multi-tool-use
- structured-output
- llm-agents
pretty_name: Kazakh Tool Output Interpretation Dataset
size_categories:
- 1K<n<10K
---

# 🇰🇿 Kazakh Tool Output Interpretation and Financial Action Dataset

## Dataset Summary

**Kazakh Tool Output Interpretation and Financial Action Dataset** is a Kazakh-language dataset designed for training and evaluating Large Language Models (LLMs) in tool-augmented agentic workflows that require interpreting structured tool outputs and generating grounded final responses.

The dataset focuses on scenarios where an assistant must understand a Kazakh user request, call the appropriate tools, read structured outputs, and summarize the result accurately. Many samples involve sensitive or action-oriented domains, such as banking, account balance checking, transfers, and financial status reporting.

This dataset is useful for studying how Kazakh-language AI agents interpret tool responses, combine outputs from multiple tools, preserve numerical accuracy, and produce concise user-facing answers based on simulated external APIs.

---

## 📊 Dataset Statistics

### General Metrics

| Metric | Count |
| :--- | :--- |
| **Total Samples** | 3,568 |
| **Total Words** (approx.) | 1,079,862 |
| **Avg. Words per Sample** | 302 |

### 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** | 29.3 | 26.0 | 8 | 137 | 104,465 |
| **category** | 1.0 | 1.0 | 1 | 1 | 3,568 |
| **difficulty** | 1.0 | 1.0 | 1 | 1 | 3,568 |
| **id** | 1.0 | 1.0 | 1 | 1 | 3,568 |
| **query** | 15.3 | 15.0 | 3 | 38 | 54,731 |
| **tools** | 88.7 | 86.0 | 30 | 160 | 316,565 |
| **turns** | 166.3 | 160.0 | 66 | 369 | 593,397 |


![image](https://cdn-uploads.huggingface.co/production/uploads/64f75f7bd04a890f5347d436/wm5t6uO4NR12_chZrKueA.png)

---

## Dataset Structure

Each dataset instance represents a complete tool-use interaction. A sample usually includes a Kazakh user query, available tool schemas, expected tool calls, simulated tool responses, and a final assistant answer.

### Data Fields

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

- **`query`**: The original user request in Kazakh. The query may involve checking information, performing a simulated action, or asking the assistant to interpret structured tool results.

- **`category`**: The task category. For example, `07_tool_output_interpretation` indicates that the sample focuses on understanding and summarizing tool outputs correctly.

- **`tools`**: A list of tools available to the assistant. Each tool contains:
  - `name`: the tool name;
  - `description`: the tool’s purpose;
  - `parameters`: the required or optional input schema;
  - `mock_response`: the expected response structure.

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

- **`answers`**: The expected tool calls. This field may contain one or more function calls with serialized JSON arguments.

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

---

## Data Instance

Below is one representative example from the dataset.

```json
{
        "id": "dastan_07_1",
        "query": "Менің 123456789 шотымдағы ақшаны 987654321 шотына 10000 теңге аударшы.",
        "category": "07_tool_output_interpretation",
        "tools": [
            {
                "name": "bank.balance",
                "description": "Get bank account balance",
                "parameters": {
                    "account": {
                        "type": "string",
                        "description": "Account number",
                        "required": true
                    },
                    "api_key": {
                        "type": "string",
                        "description": "Auth key",
                        "required": false
                    }
                },
                "mock_response": {
                    "account": "",
                    "balance": null,
                    "currency": ""
                }
            },
            {
                "name": "bank.transfer",
                "description": "Transfer money between accounts",
                "parameters": {
                    "from_account": {
                        "type": "string",
                        "description": "Source account",
                        "required": true
                    },
                    "to_account": {
                        "type": "string",
                        "description": "Destination account",
                        "required": true
                    },
                    "amount": {
                        "type": "float",
                        "description": "Amount to transfer",
                        "required": true
                    },
                    "api_key": {
                        "type": "string",
                        "description": "Auth key",
                        "required": true
                    }
                },
                "mock_response": {
                    "transaction_id": "",
                    "status": "",
                    "amount": null,
                    "currency": "",
                    "remaining_balance": null
                }
            }
        ],
        "difficulty": "easy",
        "answers": [
            {
                "name": "bank.balance",
                "arguments": "{\"account\": \"123456789\", \"api_key\": \"example-key\"}"
            },
            {
                "name": "bank.transfer",
                "arguments": "{\"from_account\": \"123456789\", \"to_account\": \"987654321\", \"amount\": 10000.0, \"api_key\": \"example-key\"}"
            }
        ],
        "turns": [
            {
                "role": "user",
                "content": "Менің 123456789 шотымдағы ақшаны 987654321 шотына 10000 теңге аударшы."
            },
            {
                "role": "assistant",
                "content": "Алдымен балансты тексеріп, сосын аударым жасаймын.",
                "meta": {
                    "plan": "Extract and normalize bank.balance + bank.transfer data",
                    "tags": [],
                    "difficulty": "hard"
                }
            },
            {
                "role": "assistant",
                "content": "",
                "tool_call": {
                    "name": "bank.balance",
                    "arguments": "{\"account\": \"123456789\", \"api_key\": \"example-key\"}"
                }
            },
            {
                "role": "tool",
                "content": "{\"account\": \"123456789\", \"balance\": 25000.0, \"currency\": \"KZT\"}"
            },
            {
                "role": "assistant",
                "content": "",
                "tool_call": {
                    "name": "bank.transfer",
                    "arguments": "{\"from_account\": \"123456789\", \"to_account\": \"987654321\", \"amount\": 10000.0, \"api_key\": \"example-key\"}"
                }
            },
            {
                "role": "tool",
                "content": "{\"transaction_id\": \"tr12345\", \"status\": \"success\", \"amount\": 10000.0, \"currency\": \"KZT\", \"remaining_balance\": 15000.0}"
            },
            {
                "role": "assistant",
                "content": "123456789 шотынан 987654321 шотына 10000 теңге сәтті аударылды. Қалған баланс: 15000 теңге. (Дерек көзі: bank.balance + bank.transfer)"
            }
        ]
    },
```

## 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}
}
```