---
dataset_info:
- config_name: nug
  features:
  - name: question
    dtype: string
  - name: answer
    dtype: string
  - name: distractor1
    dtype: string
  - name: distractor2
    dtype: string
  splits:
  - name: train
    num_bytes: 7269
    num_examples: 31
  download_size: 6720
  dataset_size: 7269
- config_name: wag
  features:
  - name: word
    dtype: string
  - name: antonym
    dtype: string
  - name: distractor1
    dtype: string
  - name: distractor2
    dtype: string
  - name: distractor3
    dtype: string
  splits:
  - name: train
    num_bytes: 3378
    num_examples: 50
  download_size: 4710
  dataset_size: 3378
- config_name: wsm
  features:
  - name: index
    dtype: int64
  - name: word
    dtype: string
  - name: synonym
    dtype: string
  - name: distractor1
    dtype: string
  - name: distractor2
    dtype: string
  - name: distractor3
    dtype: string
  splits:
  - name: train
    num_bytes: 55784
    num_examples: 475
  download_size: 36639
  dataset_size: 55784
- config_name: wub
  features:
  - name: statement
    dtype: string
  - name: check
    dtype: string
  splits:
  - name: train
    num_bytes: 2123
    num_examples: 28
  download_size: 2239
  dataset_size: 2123
- config_name: wum
  features:
  - name: question
    dtype: string
  - name: answer
    dtype: string
  - name: A
    dtype: string
  - name: B
    dtype: string
  - name: C
    dtype: string
  splits:
  - name: train
    num_bytes: 3311
    num_examples: 22
  download_size: 4301
  dataset_size: 3311
configs:
- config_name: nug
  data_files:
  - split: train
    path: nug/train-*
- config_name: wag
  data_files:
  - split: train
    path: wag/train-*
- config_name: wsm
  data_files:
  - split: train
    path: wsm/train-*
- config_name: wub
  data_files:
  - split: train
    path: wub/train-*
- config_name: wum
  data_files:
  - split: train
    path: wum/train-*
license: mit
task_categories:
- text-generation
- text-classification
- question-answering
language:
- ug
size_categories:
- n<1K
---

## Introduction
The ULUT (Uyghur language understanding test) dataset is aimed to evaluate LLM'm ability to understand Uyghur language.  All the data are crowdsourced from open materials on the Internet. Currently there are 5 types of datasets: 1. wub 2. wum 3. nug 4. wsm 5. wag

### 1. WUB
This is a boolean type word understanding dataset. This dataset evaluates LLM's ability to use right word in right context.

### 2. WUM
This is a multiple-choice word understanding dataset that tests LLM's ability to use words correctly.

### 3. NUG
This is a generative dataset that tests LLM's ability to understand the basic natural events on Earth.

### 4. WSM
This is a multiple-choice dataset that tests LLM's ability to choose the synonym for a given word.

### 5. WAG
This is a generative dataset that tests LLM's ability to generate antonym for a given word.

## Links
Evaluate your favorite LLM with this dataset using this [Colab Notebook](https://colab.research.google.com/drive/1j6hIQy8SEJ7QsEw97at2oFr74T64g0l2?usp=sharing).