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IndoMMLU

Fajri Koto, Nurul Aisyah, Haonan Li, Timothy Baldwin

πŸ“„ Paper β€’ πŸ† Leaderboard β€’ πŸ€— Dataset

Introduction

We introduce IndoMMLU, the first multi-task language understanding benchmark for Indonesian culture and languages, which consists of questions from primary school to university entrance exams in Indonesia. By employing professional teachers, we obtain 14,906 questions across 63 tasks and education levels, with 46% of the questions focusing on assessing proficiency in the Indonesian language and knowledge of nine local languages and cultures in Indonesia.

Subjects

Level Subjects
SD (Primary School) Science, Social science, Civics, Indonesian Language, Balinese, Makassarese, Banjarese, Lampungic, Madurese, Sundanese, Javanese, Dayak Ngaju, Minangkabau culture, Art, Sports, Islam religion, Christian religion, Hindu religion
SMP (Junior High School) Science, Social science, Civics, Indonesian Language, Balinese, Makassarese, Banjarese, Lampungic, Madurese, Sundanese, Javanese, Minangkabau culture, Art, Sports, Islam religion, Christian religion, Hindu religion
SMA (Senior High School) Physics, Chemistry, Biology, Geography, Sociology, Economics, History, Civics, Indonesian Language, Balinese, Makassarese, Banjarese, Lampungic, Madurese, Sundanese, Javanese, Art, Sports, Islam religion, Christian religion, Hindu religion
University Entrance Test Chemistry, Biology, Geography, Sociology, Economics, History, Indonesian Language

We categorize the collected questions into different subject areas, including: (1) STEM (Science, Technology, Engineering, and Mathematics); (2) Social Science; (3) Humanities; (4) Indonesian Language; and (5) Local Languages and Cultures.

Examples

These questions are written in Indonesian. For local language subjects, some are written in the local languages. The English version is for illustrative purposes only.

Evaluation

We evaluate 24 multilingual LLMs of different sizes in zero-shot and few-shot settings. This includes GPT-3.5 (ChatGPT), XGLM, Falcon, BLOOMZ, mT0, LLaMA, and Bactrian-X. Prior to the question and multiple-choice options, we add a simple prompt in the Indonesian language:

 Ini adalah soal [subject] untuk [level]. Pilihlah salah satu jawaban yang dianggap benar!
 English Translation: This is a [subject] question for [level]. Please choose the correct answer!

Zero-shot Evaluation

Model (#param) STEM Social Science Humanities Indonesian Lang. Local L. Culture Average
Random 21.9 23.4 23.5 24.4 26.6 24.4
GPT-3.5 (175B) 54.3 62.5 64.0 62.2 39.3 53.2
XGLM (564M) 22.1 23.0 25.6 25.6 27.5 25.2
XGLM (1.7B) 20.9 23.0 24.6 24.8 26.6 24.4
XGLM (2.9B) 22.9 23.2 25.4 26.3 27.2 25.2
XGLM (4.5B) 21.8 23.1 25.6 25.8 27.1 25.0
XGLM (7.5B) 22.7 21.7 23.6 24.5 27.5 24.5
Falcon (7B) 22.1 22.9 25.5 25.7 27.5 25.1
Falcon (40B) 30.2 34.8 34.8 34.9 29.2 32.1
BLOOMZ (560M) 22.9 23.6 23.2 24.2 25.1 24.0
BLOOMZ (1.1B) 20.4 21.4 21.1 23.5 24.7 22.4
BLOOMZ (1.7B) 31.5 39.3 38.3 42.8 29.4 34.4
BLOOMZ (3B) 33.5 44.5 39.7 46.7 29.8 36.4
BLOOMZ (7.1B) 37.1 46.7 44.0 49.1 28.2 38.0
mT0small (300M) 21.8 21.4 25.7 25.1 27.6 24.9
mT0base (580M) 22.6 22.6 25.7 25.6 26.9 25.0
mT0large (1.2B) 22.0 23.4 25.1 27.3 27.6 25.2
mT0xl (3.7B) 31.4 42.9 41.0 47.8 35.7 38.2
mT0xxl (13B) 33.5 46.2 47.9 52.6 39.6 42.5
LLaMA (7B) 22.8 23.1 25.1 26.7 27.6 25.3
LLaMA (13B) 24.1 23.0 24.4 29.5 26.7 25.3
LLaMA (30B) 25.4 23.5 25.9 28.4 28.7 26.5
LLaMA (65B) 33.0 37.7 40.8 41.4 32.1 35.8
Bactrian-X-LLaMA (7B) 23.3 24.0 26.0 26.1 27.5 25.7
Bactrian-X-LLaMA (13B) 28.3 29.9 32.8 35.2 29.2 30.3

GPT-3.5 performance (% accuracy) across different education levels

Red indicates that the score is below the minimum passing threshold of 65, while green signifies a score at or above this minimum. We can observe that ChatGPT mostly passes a score of 65 in Indonesian primary school exams.

Few-shot Evaluation

Data

Each question in the dataset is a multiple-choice question with up to 5 choices and only one choice as the correct answer. We provide our dataset according to each subject in data folder. You can also access our dataset via Hugging Face.

Evaluation

The code for the evaluation of each model we used is in evaluate.py, and the code to run them is listed in run.sh.

Citation

@inproceedings{koto-etal-2023-indommlu,
    title = "Large Language Models Only Pass Primary School Exams in {I}ndonesia: A Comprehensive Test on {I}ndo{MMLU}",
    author = "Fajri Koto and Nurul Aisyah and Haonan Li and Timothy Baldwin",
    booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = December,
    year = "2023",
    address = "Singapore",
    publisher = "Association for Computational Linguistics",
}

License

The IndoMMLU dataset is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

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