MalayMMLU

Released on September 27, 2024

English | Bahasa Melayu

📄 Paper • Code • 📜 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
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
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
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

Highest scores are bolded and second highest scores are underlined. † denotes LLMs fine-tuned with Southeast Asia datasets. †† denotes open-source GLM-4. ‡ result from https://mesolitica.com/mallam.

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.