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[MalayMMLU] This is the first-ever Bahasa Melayu multitask benchmark designed to elevate the performance of Large Language Models (LLMs) and Large Vision Language Models (LVLMs).

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MalayMMLU: A Multitask Benchmark for the Low-Resource Malay Language (Official site)

Released on September 27, 2024

English | Bahasa Melayu

📄 Paper (EMNLP2024) • 🤗 Dataset • 📜 Poster

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
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-3.1 (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
Google 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
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
Mesolitica MaLLaM-v2† (5B) 42.57 46.44 42.24 40.82 38.74 42.08
Yellow.ai Komodo† (7B) 43.62 45.53 39.34 39.75 39.48 41.72
Highest scores are bolded and second highest scores are underlined. † denotes LLMs fine-tuned with Southeast Asia datasets. †† denotes open-source GLM-4.

Few-shot results of LLMs on MalayMMLU (First token accuracy)

Installation

git clone https://github.com/UMxYTL-AI-Labs/MalayMMLU.git
cd MalayMMLU
pip install -r requirements.txt

Evaluation

We provide example evaluation scripts in scripts

usage: evaluate.py [-h] [--by_letter] --base_model BASE_MODEL --output_folder OUTPUT_FOLDER [--playground PLAYGROUND] [--task TASK] [--shot SHOT] [--token TOKEN]
options:
  -h, --help            show this help message and exit
  --by_letter           Use this flag to calculate first token accuracy
  --base_model BASE_MODEL
                        Path to pretrained model
  --output_folder OUTPUT_FOLDER
                        Folder where the output will be saved
  --playground PLAYGROUND
                        Set this to True to enable playground mode (default: False).
  --task TASK           Specify the task to be executed (default: 'MalayMMLU').
  --shot SHOT           Provide the number of shots: 0,1,2 or 3 (default: 0).
  --token TOKEN         Specify the HuggingFace token

Evaluation by first token accuracy for LLM

  • PRED_FILE: filename of prediction file
    • For example, "output/MalayMMLU_result_Meta-Llama-3-8B-Instruct_True_0shot.csv"
SHOT=0
# prediction
python src/evaluate.py  --by_letter --shot $SHOT  --task=MalayMMLU \
                    --base_model=meta-llama/Meta-Llama-3-8B-Instruct  \
                    --output_folder=output/ --token $TOKEN

# calculate accuracy
PRED_FILE=output/MalayMMLU_result_Meta-Llama-3-8B-Instruct_True_0shot.csv

python src/calculate_accuracies.py --pred_files $PRED_FILE \
    --data_file=$SHOT \
    --output_dir=output/

# calculate accuracy for all prediction files in a folder

PRED_DIR=output/
python src/calculate_accuracies.py --all --pred_dir  $PRED_DIR \
    --shot=$SHOT \
    --output_dir=results/

Evaluation by full answer probability for LLM

# calculate accuracy
python src/evaluate.py  --shot $SHOT True  --task=MalayMMLU \
                    --base_model=meta-llama/Meta-Llama-3-8B-Instruct  \
                    --output_folder=output/ --token $TOKEN

# calculate accuracy                  
PRED_FILE=output/MalayMMLU_result_Meta-Llama-3-8B-Instruct_False_0shot.csv

python src/calculate_accuracies.py --pred_files $PRED_FILE \
    --shot=$SHOT \
    --output_dir=output/

# calculate accuracy for all prediction files in a folder
PRED_DIR=output/

python src/calculate_accuracies.py --all --pred_dir  $PRED_DIR \
    --shot=$SHOT \
    --output_dir=output/

Evaluation for LVLM

The steps and usage are similar for evaluate_pixtral.py, evaluate_qwen_vl.py, evaluate_intern_vl.py

Evaluation for Closed Source Models

  • API_KEY: OpenAI API key
# prediction
python src/evaluate_gpt.py --model gpt-3.5-turbo --api_key $API_KEY --shot $SHOT
  • Download the prediction file (jsonl file ) from OpenAI platform
  • Rename the file in following format: MalayMMLU_{$MODEL}_{$SHOT}shot.jsonl
    • Example: MalayMMLU_gpt3_0shot.jsonl
# calculate accurcacy
python src/calculate_accuracies.py --pred_files $PRED_FILE \
    --shot=$SHOT \
    --output_dir=output/ --closed

# calculate accuracy for all prediction files in a folder
python src/calculate_accuracies.py --all --pred_dir  $PRED_DIR \
    --shot=$SHOT \
    --output_dir=output/ --closed

The steps and usage are similar for evaluate_glm.py

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.

Acknowledgement

The code base is built upon IndoMMLU

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[MalayMMLU] This is the first-ever Bahasa Melayu multitask benchmark designed to elevate the performance of Large Language Models (LLMs) and Large Vision Language Models (LVLMs).

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