farabi-lab/Final_Answer_Synthesis_with_Provenance

🇰🇿 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.

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📊 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

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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.

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Data Instance

Below is one representative example from the dataset.

"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

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}
}