MiLiC-Eval

MiLiC-Eval is an NLP evaluation suite for Minority Languages in China, covering Tibetan (bo), Uyghur (ug), Kazakh (kk, in the Kazakh Arabic script), and Mongolian (mn, in the traditional Mongolian script).

Statistics

Tasks

Currently, MiLiC-Eval consists of 9 tasks and 4 languages, with a total of 24K instances. The statistics of each task are shown in the following table.

Task	Size	Metric	Languages
Vocabulary Understanding	1,000/lang	Accuracy	bo, ug, kk, mn
Topic Classification (Sentence)	492/lang	Accuracy	bo, ug, kk, mn, zh, en
Topic Classification (Passage)	600/lang	Accuracy	bo, ug, kk, mn
Reading Comprehension	250/lang	Accuracy	bo, ug, kk, mn, zh, en
Response Selection	507/lang	Accuracy	bo, ug, kk, mn, zh, en
Title Generation	1,000/lang	ROUGE-L	bo, ug, kk, mn
Machine Translation (Article)	1,012/lang	chrF++	bo, ug, kk, mn, zh, en
Machine Translation (Dialogue)	773/lang	chrF++	bo, ug, kk, mn, zh, en
Math Reasoning	250/lang	Accuracy	bo, ug, kk, mn, zh, en

Data Splits

For each task, we provide a data split, including training, development, and test sets.

The training sets are small and used for in-context learning. For each task, we provide three training sets sampled with different seeds, to reduce the impact of randomness during prompting.

The development sets are used for hyperparameter tuning. The test sets are used for evaluation.

For each language, the data split is shown in the following table.

Task	Train	Dev	Test
Vocabulary Understanding	20 * 3	40	900
Topic Classification (Sentence)	10 * 3	30	432
Topic Classification (Passage)	16 * 3	48	504
Reading Comprehension	10 * 3	20	200
Response Selection	20 * 3	40	407
Title Generation	20 * 3	40	900
Machine Translation (Article)	20 * 3	40	912
Machine Translation (Dialogue)	20 * 3	40	673
Math Reasoning	10 * 3	20	200

Usage

See steps of using the data on GitHub

Pretraining Corpus

Current LLMs have limited performance on minority languages due to the lack of pretraining data. We provide a pretraining corpus, MC^2 for the four languages in MiLiC-Eval.

The corpus can be downloaded from Hugging Face. You can read the details of the corpus in our paper MC^2: Towards Transparent and Culturally-Aware NLP for Minority Languages in China (ACL 2024).

Citation

If you use MiLiC-Eval in your research, please cite our GitHub repository:

@misc{milic_eval,
  author = {Zhang, Chen and Tao, Mingxu and Feng, Yansong},
  title = {{MiLiC-Eval}: {A}n {NLP} {E}valuation {S}uite for {M}inority {L}anguages in {C}hina},
  year = {2024},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/luciusssss/MiLiC-Eval}},
}