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.