---
dataset_info:
  features:
  - name: category_english
    dtype: string
  - name: category_kazakh
    dtype: string
  - name: item_description
    dtype: string
  - name: question
    dtype: string
  - name: answer
    dtype: string
  - name: source
    dtype: string
  - name: set
    dtype: string
  - name: a
    dtype: string
  - name: b
    dtype: string
  - name: c
    dtype: string
  - name: d
    dtype: string
  - name: answer_label
    dtype: string
  splits:
  - name: train
    num_bytes: 17193789
    num_examples: 14803
  - name: test
    num_bytes: 1202406
    num_examples: 1334
  download_size: 4640071
  dataset_size: 18396195
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: test
    path: data/test-*
license: cc-by-4.0
task_categories:
- question-answering
language:
- kk
size_categories:
- 10K<n<100K
---

# Dataset Card for KazCulture

## Dataset Summary
<!-- Provide a quick summary of the dataset. -->

KazCulture is a culturally focused Kazakh dataset consisting of 16,137 human-crafted passage–question–answer (PQA) triplets designed to evaluate and enhance large language models’ understanding of Kazakh cultural knowledge. The passages were collected from 11 authoritative books on Kazakh culture and the Koshpendiler.kz website, covering topics such as customs and traditions, traditional music and instruments, beliefs and superstitions, household practices, cuisine, national games, clothing, and handicrafts. Each example pairs a culturally rich passage with a question and a concise answer, enabling both evaluation and supervised fine-tuning.

KazCulture can be used as a benchmark for assessing cultural competence in LLMs and as a training resource for improving cultural understanding.

## Dataset Structure

<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->

The KazCulture dataset consists of two splits—train and test—each containing human-crafted passage–question–answer (PQA) pairs about Kazakh culture. The train split includes only the core fields used for supervised fine-tuning, while the test split includes multiple-choice options for evaluation.


### Train Split

The `train` split contains PQA triplets without multiple-choice options.
Each example includes:

* category_english: the cultural domain of the passage, in English (e.g., *Clothing*, *Cuisine*, *Musical instruments*), stored as a string.
* category_kazakh: the same cultural domain in Kazakh.
* item_description: the passage describing a cultural item, tradition, or concept, stored as a string.
* question: the comprehension question about the passage, stored as a string.
* answer: the correct short answer to the question, stored as a string.
* source: the name of the book or website from which the passage was taken, stored as a string.
* set: the split identifier (`train`).

**Note:** The `train` split does not include multiple-choice fields (`a`, `b`, `c`, `d`) and does not include `answer_label`.


### Test Split

The `test` split is designed for multiple-choice evaluation.
Each example includes all fields from the train split, plus the following:

* a: option A (string)
* b: option B (string)
* c: option C (string)
* d: option D (string)
* answer_label: the correct answer label (`"a"`, `"b"`, `"c"`, or `"d"`), stored as a string.
* set: the split identifier (`test`).