| --- |
| language: |
| - kk |
| license: cc-by-nc-4.0 |
| task_categories: |
| - text-generation |
| - question-answering |
| tags: |
| - kazakh |
| - agentic-ai |
| - tool-use |
| - function-calling |
| - multi-tool-use |
| - planning |
| - multistep-reasoning |
| - task-composition |
| - travel-assistant |
| - currency-conversion |
| - llm-agents |
| pretty_name: Kazakh Multi-Step Planning and Tool Composition Dataset |
| size_categories: |
| - 1K<n<10K |
| --- |
| |
| # 🇰🇿 Kazakh Multi-Step Planning and Tool Composition Dataset |
|
|
| ## Dataset Summary |
|
|
| **Kazakh Multi-Step Planning and Tool Composition Dataset** is a Kazakh-language dataset for training and evaluating Large Language Models (LLMs) in agentic AI workflows that require multi-step planning, tool composition, and structured function calling. |
|
|
| The dataset contains user requests, available tool schemas, expected tool calls, simulated tool responses, and complete multi-turn interaction traces. It focuses on scenarios where a model must break down a user request into several connected actions, call the appropriate tools in the correct order, combine outputs from different tools, and generate a final answer in Kazakh. |
|
|
| This dataset is useful for developing Kazakh-language AI agents capable of handling practical workflows such as travel planning, ticket search, currency conversion, scheduling, information lookup, and other multi-tool tasks. |
|
|
| --- |
|
|
| ## 📊 Dataset Statistics |
|
|
| ### General Metrics |
|
|
| | Metric | Count | |
| | :--- | :--- | |
| | **Total Samples** | 3,025 | |
| | **Total Words** (approx.) | 943,485 | |
| | **Avg. Words per Sample** | 311 | |
|
|
| ### 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** | 32.5 | 27.0 | 13 | 134 | 98,398 | |
| | **category** | 1.0 | 1.0 | 1 | 1 | 3,025 | |
| | **difficulty** | 1.0 | 1.0 | 1 | 1 | 3,025 | |
| | **id** | 1.0 | 1.0 | 1 | 1 | 3,025 | |
| | **query** | 15.0 | 14.0 | 3 | 46 | 45,357 | |
| | **tools** | 91.5 | 90.0 | 50 | 200 | 276,832 | |
| | **turns** | 169.9 | 163.0 | 103 | 417 | 513,823 | |
|
|
|
|
|  |
|
|
| --- |
|
|
| ## Dataset Structure |
|
|
| Each dataset instance represents a complete agentic workflow. A sample usually contains a Kazakh user request, the tools available to the assistant, the expected tool calls, mock tool responses, and the final assistant answer. |
|
|
| ### Data Fields |
|
|
| - **`id`**: A unique identifier for the sample. |
|
|
| - **`query`**: The original user request in Kazakh. The request often requires several connected steps, such as searching for transport information and then converting prices into another currency. |
|
|
| - **`category`**: The task category. For example, `03_planning_multistep_composition` indicates that the sample focuses on planning, multi-step reasoning, and tool composition. |
|
|
| - **`tools`**: A list of tools available to the assistant. Each tool includes: |
| - `name`: the tool name; |
| - `description`: a short explanation of the tool’s function; |
| - `parameters`: the required or optional input schema; |
| - `mock_response`: the expected response structure. |
|
|
| - **`difficulty`**: The difficulty level of the sample. In this dataset, multi-step composition tasks may involve harder reasoning because the assistant must coordinate several tools. |
|
|
| - **`answers`**: The target tool calls expected from the assistant. This field usually contains multiple tool calls with serialized JSON arguments. |
|
|
| - **`turns`**: The full conversation trajectory, including: |
| - user request; |
| - assistant planning step; |
| - assistant tool calls; |
| - mock tool outputs; |
| - final answer grounded in the tool responses. |
|
|
| --- |
|
|
| ## Data Instance |
|
|
| Below is one representative example from the dataset. |
|
|
| ```json |
| { |
| "id": "aikerim_id_1", |
| "query": "Алматыдан Астанаға 2024-07-01 күні жүретін пойыздардың бағасын тауып, купе билетін доллармен есептеп бер.", |
| "category": "03_planning_multistep_composition", |
| "tools": [ |
| { |
| "name": "trains.search", |
| "description": "Search train schedules", |
| "parameters": { |
| "from": { |
| "type": "string", |
| "description": "Departure station", |
| "required": true |
| }, |
| "to": { |
| "type": "string", |
| "description": "Arrival station", |
| "required": true |
| }, |
| "date": { |
| "type": "string", |
| "description": "Travel date YYYY-MM-DD", |
| "required": true |
| } |
| }, |
| "mock_response": { |
| "trains": [ |
| { |
| "number": "", |
| "type": "", |
| "departure": "", |
| "arrival": "", |
| "price_coupe": null, |
| "price_platz": null |
| } |
| ] |
| } |
| }, |
| { |
| "name": "forex.rate", |
| "description": "Get currency exchange rate", |
| "parameters": { |
| "from": { |
| "type": "string", |
| "description": "Source currency code", |
| "required": true |
| }, |
| "to": { |
| "type": "string", |
| "description": "Target currency code", |
| "required": true |
| } |
| }, |
| "mock_response": { |
| "from": "", |
| "to": "", |
| "rate": null, |
| "updated": "" |
| } |
| } |
| ], |
| "difficulty": "hard", |
| "answers": [ |
| { |
| "name": "trains.search", |
| "arguments": "{\"from\": \"Алматы\", \"to\": \"Астана\", \"date\": \"2024-07-01\"}" |
| }, |
| { |
| "name": "forex.rate", |
| "arguments": "{\"from\": \"KZT\", \"to\": \"USD\"}" |
| } |
| ], |
| "turns": [ |
| { |
| "role": "user", |
| "content": "Алматыдан Астанаға 2024-07-01 күні жүретін пойыздардың бағасын тауып, купе билетін доллармен есептеп бер." |
| }, |
| { |
| "role": "assistant", |
| "content": "Алдымен пойыз бағасын іздеп, кейін доллар бағамын анықтаймын.", |
| "meta": { |
| "plan": "trains.search + forex.rate connection fetching parameters to query", |
| "tags": [], |
| "difficulty": "hard" |
| } |
| }, |
| { |
| "role": "assistant", |
| "content": "", |
| "tool_call": { |
| "name": "trains.search", |
| "arguments": "{\"from\": \"Алматы\", \"to\": \"Астана\", \"date\": \"2024-07-01\"}" |
| } |
| }, |
| { |
| "role": "tool", |
| "content": "{\"trains\": [{\"number\": \"001Т\", \"type\": \"Тальго\", \"departure\": \"2024-07-01 20:00\", \"arrival\": \"2024-07-02 08:00\", \"price_coupe\": 18000, \"price_platz\": 9500}]}" |
| }, |
| { |
| "role": "assistant", |
| "content": "", |
| "tool_call": { |
| "name": "forex.rate", |
| "arguments": "{\"from\": \"KZT\", \"to\": \"USD\"}" |
| } |
| }, |
| { |
| "role": "tool", |
| "content": "{\"from\": \"KZT\", \"to\": \"USD\", \"rate\": 450.5, \"updated\": \"2024-06-25T10:00:00Z\"}" |
| }, |
| { |
| "role": "assistant", |
| "content": "Алматыдан Астанаға 2024-07-01 күнгі \"001Т Тальго\" пойызының купе билеті 18000 теңге тұрады. Бұл шамамен 40 доллар (айырбас бағамы 1 USD = 450.5 KZT) болады. (Дерек көзі: trains.search + forex.rate)" |
| } |
| ] |
| } |
| ``` |
|
|
| ## 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} |
| } |
| ``` |
|
|
