metadata
task_categories:
- question-answering
language:
- ms
tags:
- knowledge
pretty_name: MalayMMLU
size_categories:
- 10K<n<100K
configs:
- config_name: default
data_files:
- split: eval
path:
- MalayMMLU_0shot.json
- MalayMMLU_1shot.json
- MalayMMLU_2shot.json
- MalayMMLU_3shot.json
MalayMMLU
Released on September 27, 2024
English | Bahasa Melayu
Introduction
MalayMMLU is the first multitask language understanding (MLU) for Malay Language. The benchmark comprises 24,213 questions spanning both primary (Year 1-6) and secondary (Form 1-5) education levels in Malaysia, encompassing 5 broad topics that further divide into 22 subjects.
| Category | Subjects |
|---|---|
| STEM | Computer Science (Secondary), Biology (Secondary), Chemistry (Secondary), Computer Literacy (Secondary), Mathematics (Primary, Secondary), Additional Mathematics (Secondary), Design and Technology (Primary, Secondary), Core Science (Primary, Secondary), Information and Communication Technology (Primary), Automotive Technology (Secondary) |
| Language | Malay Language (Primary, Secondary) |
| Social science | Geography (Secondary), Local Studies (Primary), History (Primary, Secondary) |
| Others | Life Skills (Primary, Secondary), Principles of Accounting (Secondary), Economics (Secondary), Business (Secondary), Agriculture (Secondary) |
| Humanities | Quran and Sunnah (Secondary), Islam (Primary, Secondary), Sports Science Knowledge (Secondary) |
Result
Zero-shot results of LLMs on MalayMMLU (First token accuracy)
| Organization | Model | Vision | Acc. | ||||||
|---|---|---|---|---|---|---|---|---|---|
| Language | Humanities | STEM | Social Science | Others | Average | ||||
| Random | 38.01 | 42.09 | 36.31 | 36.01 | 38.07 | 38.02 | |||
| YTL | Ilmu 0.1 | 87.77 | 89.26 | 86.66 | 85.27 | 86.40 | 86.98 | ||
| OpenAI | GPT-4o | ✓ | 87.12 | 88.12 | 83.83 | 82.58 | 83.09 | 84.98 | |
| GPT-4 | ✓ | 82.90 | 83.91 | 78.80 | 77.29 | 77.33 | 80.11 | ||
| GPT-4o mini | ✓ | 82.03 | 81.50 | 78.51 | 75.67 | 76.30 | 78.78 | ||
| GPT-3.5 | 69.62 | 71.01 | 67.17 | 66.70 | 63.73 | 67.78 | |||
| Meta | LLaMA-3.1 (70B) | 78.75 | 82.59 | 78.96 | 77.20 | 75.32 | 78.44 | ||
| LLaMA-3.3 (70B) | 78.82 | 80.46 | 78.71 | 75.79 | 73.85 | 77.38 | |||
| LLaMA-3.1 (8B) | 65.47 | 67.17 | 64.10 | 62.59 | 62.13 | 64.24 | |||
| LLaMA-3 (8B) | 63.93 | 66.21 | 62.26 | 62.97 | 61.38 | 63.46 | |||
| LLaMA-2 (13B) | 45.58 | 50.72 | 44.13 | 44.55 | 40.87 | 45.26 | |||
| LLaMA-2 (7B) | 47.47 | 52.74 | 48.71 | 50.72 | 48.19 | 49.61 | |||
| LLaMA-3.2 (3B) | 58.52 | 60.66 | 56.65 | 54.06 | 52.75 | 56.45 | |||
| LLaMA-3.2 (1B) | 38.88 | 43.30 | 40.65 | 40.56 | 39.55 | 40.46 | |||
| Qwen (Alibaba) | Qwen 2.5 (72B) | 79.09 | 79.95 | 80.88 | 75.80 | 75.05 | 77.79 | ||
| Qwen-2.5 (32B) | 76.96 | 76.70 | 79.74 | 72.35 | 70.88 | 74.83 | |||
| Qwen-2-VL (7B) | ✓ | 68.16 | 63.62 | 67.58 | 60.38 | 59.08 | 63.49 | ||
| Qwen-2-VL (2B) | ✓ | 58.22 | 55.56 | 57.51 | 53.67 | 55.10 | 55.83 | ||
| Qwen-1.5 (14B) | 64.47 | 60.64 | 61.97 | 57.66 | 58.05 | 60.47 | |||
| Qwen-1.5 (7B) | 60.13 | 59.14 | 58.62 | 54.26 | 54.67 | 57.18 | |||
| Qwen-1.5 (4B) | 48.39 | 52.01 | 51.37 | 50.00 | 49.10 | 49.93 | |||
| Qwen-1.5 (1.8B) | 42.70 | 43.37 | 43.68 | 43.12 | 44.42 | 43.34 | |||
| Zhipu | GLM-4-Plus | 78.04 | 75.63 | 77.49 | 74.07 | 72.66 | 75.48 | ||
| GLM-4-Air | 67.88 | 69.56 | 70.20 | 66.06 | 66.18 | 67.60 | |||
| GLM-4-Flash | 63.52 | 65.69 | 66.31 | 63.21 | 63.59 | 64.12 | |||
| GLM-4 | 63.39 | 56.72 | 54.40 | 57.24 | 55.00 | 58.07 | |||
| GLM-4†† (9B) | 58.51 | 60.48 | 56.32 | 55.04 | 53.97 | 56.87 | |||
| Gemma-2 (9B) | 75.83 | 72.83 | 75.07 | 69.72 | 70.33 | 72.51 | |||
| Gemma (7B) | 45.53 | 50.92 | 46.13 | 47.33 | 46.27 | 47.21 | |||
| Gemma (2B) | 46.50 | 51.15 | 49.20 | 48.06 | 48.79 | 48.46 | |||
| SAIL (Sea) | Sailor† (14B) | 78.40 | 72.88 | 69.63 | 69.47 | 68.67 | 72.29 | ||
| Sailor† (7B) | 74.54 | 68.62 | 62.79 | 64.69 | 63.61 | 67.58 | |||
| Mesolitica | MaLLaM-v2.5 Small‡ | 73.00 | 71.00 | 70.00 | 72.00 | 70.00 | 71.53 | ||
| MaLLaM-v2.5 Tiny‡ | 67.00 | 66.00 | 68.00 | 69.00 | 66.00 | 67.32 | |||
| MaLLaM-v2† (5B) | 42.57 | 46.44 | 42.24 | 40.82 | 38.74 | 42.08 | |||
| Cohere for AI | Command R (32B) | 71.68 | 71.49 | 66.68 | 67.19 | 63.64 | 68.47 | ||
| OpenGVLab | InternVL2 (40B) | ✓ | 70.36 | 68.49 | 64.88 | 65.93 | 60.54 | 66.51 | |
| Damo (Alibaba) | SeaLLM-v2.5† (7B) | 69.75 | 67.94 | 65.29 | 62.66 | 63.61 | 65.89 | ||
| Mistral | Pixtral (12B) | ✓ | 64.81 | 62.68 | 64.72 | 63.93 | 59.49 | 63.25 | |
| Mistral Small (22B) | 65.19 | 65.03 | 63.36 | 61.58 | 59.99 | 63.05 | |||
| Mistral-v0.3 (7B) | 56.97 | 59.29 | 57.14 | 58.28 | 56.56 | 57.71 | |||
| Mistral-v0.2 (7B) | 56.23 | 59.86 | 57.10 | 56.65 | 55.22 | 56.92 | |||
| Microsoft | Phi-3 (14B) | 60.07 | 58.89 | 60.91 | 58.73 | 55.24 | 58.72 | ||
| Phi-3 (3.8B) | 52.24 | 55.52 | 54.81 | 53.70 | 51.74 | 53.43 | |||
| 01.AI | Yi-1.5 (9B) | 56.20 | 53.36 | 57.47 | 50.53 | 49.75 | 53.08 | ||
| Stability AI | StableLM 2 (12B) | 53.40 | 54.84 | 51.45 | 51.79 | 50.16 | 52.45 | ||
| StableLM 2 (1.6B) | 43.92 | 51.10 | 45.27 | 46.14 | 46.75 | 46.48 | |||
| Baichuan | Baichuan-2 (7B) | 40.41 | 47.35 | 44.37 | 46.33 | 43.54 | 44.30 | ||
| Yellow.ai | Komodo† (7B) | 43.62 | 45.53 | 39.34 | 39.75 | 39.48 | 41.72 | ||
Citation
@InProceedings{MalayMMLU2024,
author = {Poh, Soon Chang and Yang, Sze Jue and Tan, Jeraelyn Ming Li and Chieng, Lawrence Leroy Tze Yao and Tan, Jia Xuan and Yu, Zhenyu and Foong, Chee Mun and Chan, Chee Seng },
title = {MalayMMLU: A Multitask Benchmark for the Low-Resource Malay Language},
booktitle = {Findings of the Association for Computational Linguistics: EMNLP 2024},
month = {November},
year = {2024},
}
Feedback
Suggestions and opinions (both positive and negative) are greatly welcome. Please contact the author by sending email to cs.chan at um.edu.my.