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TMMLU+ : Large scale traditional chinese massive multitask language understanding
We present TMMLU+, a traditional Chinese massive multitask language understanding dataset. TMMLU+ is a multiple-choice question-answering dataset featuring 66 subjects, ranging from elementary to professional level.
The TMMLU+ dataset is six times larger and contains more balanced subjects compared to its predecessor, TMMLU. We have included benchmark results in TMMLU+ from closed-source models and 20 open-weight Chinese large language models, with parameters ranging from 1.8B to 72B. The benchmark results show that Traditional Chinese variants still lag behind those trained on major Simplified Chinese models.
from datasets import load_dataset
task_list = [
'engineering_math', 'dentistry', 'traditional_chinese_medicine_clinical_medicine', 'clinical_psychology', 'technical', 'culinary_skills', 'mechanical', 'logic_reasoning', 'real_estate',
'general_principles_of_law', 'finance_banking', 'anti_money_laundering', 'ttqav2', 'marketing_management', 'business_management', 'organic_chemistry', 'advance_chemistry',
'physics', 'secondary_physics', 'human_behavior', 'national_protection', 'jce_humanities', 'politic_science', 'agriculture', 'official_document_management',
'financial_analysis', 'pharmacy', 'educational_psychology', 'statistics_and_machine_learning', 'management_accounting', 'introduction_to_law', 'computer_science', 'veterinary_pathology',
'accounting', 'fire_science', 'optometry', 'insurance_studies', 'pharmacology', 'taxation', 'trust_practice', 'geography_of_taiwan', 'physical_education', 'auditing', 'administrative_law',
'education_(profession_level)', 'economics', 'veterinary_pharmacology', 'nautical_science', 'occupational_therapy_for_psychological_disorders',
'basic_medical_science', 'macroeconomics', 'trade', 'chinese_language_and_literature', 'tve_design', 'junior_science_exam', 'junior_math_exam', 'junior_chinese_exam',
'junior_social_studies', 'tve_mathematics', 'tve_chinese_language', 'tve_natural_sciences', 'junior_chemistry', 'music', 'education', 'three_principles_of_people',
'taiwanese_hokkien',
'linear_algebra'
]
for task in task_list:
val = load_dataset('ZoneTwelve/tmmluplus', task)['validation']
dev = load_dataset('ZoneTwelve/tmmluplus', task)['train']
test = load_dataset('ZoneTwelve/tmmluplus', task)['test']
For each dataset split
for row in test:
print(row)
break
>> Dataset({
features: ['question', 'A', 'B', 'C', 'D', 'answer'],
num_rows: 11
})
Statistic on all four categories : STEM, Social Science, Humanities, Other
Category | Test | Dev | Validation |
---|---|---|---|
STEM | 3458 | 70 | 385 |
Social Sciences | 5958 | 90 | 665 |
Humanities | 1763 | 35 | 197 |
Other (Business, Health, Misc.) | 8939 | 135 | 995 |
Total | 20118 | 330 | 2242 |
Benchmark on direct prompting
model | STEM | Social Science | Humanities | Other | Average |
---|---|---|---|---|---|
Qwen/Qwen-72B | 61.12 | 71.65 | 63.00 | 61.31 | 64.27 |
gpt-4-0613 | 60.36 | 67.36 | 56.03 | 57.62 | 60.34 |
Qwen/Qwen-72B-Chat | 55.15 | 66.20 | 55.65 | 57.19 | 58.55 |
Qwen/Qwen-14B | 46.94 | 56.69 | 49.43 | 48.81 | 50.47 |
Gemini-pro | 45.38 | 57.29 | 48.80 | 48.21 | 49.92 |
01-ai/Yi-34B-Chat | 40.24 | 56.77 | 53.99 | 47.58 | 49.64 |
Qwen/Qwen-14B-Chat | 43.86 | 53.29 | 44.78 | 45.13 | 46.77 |
01-ai/Yi-6B-Chat | 39.62 | 50.24 | 44.44 | 44.26 | 44.64 |
Claude-1.3 | 42.65 | 49.33 | 42.16 | 44.14 | 44.57 |
gpt-3.5-turbo-0613 | 41.56 | 46.72 | 36.73 | 42.03 | 41.76 |
CausalLM/14B | 39.83 | 44.50 | 39.61 | 41.97 | 41.48 |
Skywork/Skywork-13B-base | 36.93 | 47.27 | 41.04 | 40.10 | 41.33 |
Qwen/Qwen-7B | 37.53 | 45.48 | 38.09 | 38.96 | 40.01 |
Qwen/Qwen-7B-Chat | 33.32 | 44.64 | 40.27 | 39.89 | 39.53 |
vivo-ai/BlueLM-7B-Base | 33.94 | 41.52 | 37.38 | 38.74 | 37.90 |
baichuan-inc/Baichuan2-13B-Chat | 29.64 | 43.73 | 37.36 | 39.88 | 37.65 |
Qwen/Qwen-1_8B | 32.65 | 38.95 | 38.34 | 35.27 | 36.30 |
Claude-2 | 39.65 | 39.09 | 28.59 | 37.47 | 36.20 |
THUDM/chatglm3-6b | 31.05 | 39.31 | 35.64 | 35.60 | 35.40 |
deepseek-ai/deepseek-llm-7b-chat | 29.82 | 42.29 | 34.24 | 34.31 | 35.17 |
CausalLM/7B | 31.03 | 38.17 | 35.87 | 35.39 | 35.11 |
Azure99/blossom-v3_1-mistral-7b | 32.80 | 36.91 | 32.36 | 34.53 | 34.15 |
microsoft/Orca-2-13b | 24.69 | 39.18 | 33.60 | 31.99 | 32.37 |
Qwen/Qwen-1_8B-Chat | 26.60 | 36.36 | 31.81 | 31.96 | 31.68 |
TigerResearch/tigerbot-13b-chat-v3 | 24.73 | 29.63 | 25.72 | 27.22 | 26.82 |
hongyin/mistral-7b-80k | 24.26 | 23.76 | 22.56 | 24.57 | 23.79 |
deepseek-ai/deepseek-llm-67b-chat | 19.10 | 26.06 | 21.51 | 21.77 | 22.11 |
yentinglin/Taiwan-LLM-13B-v2.0-chat | 18.53 | 27.65 | 17.77 | 21.49 | 21.36 |
GeneZC/MiniChat-3B | 17.66 | 23.35 | 22.71 | 20.34 | 21.02 |
LinkSoul/Chinese-Llama-2-7b | 16.55 | 18.39 | 12.97 | 16.13 | 16.01 |
yentinglin/Taiwan-LLM-7B-v2.1-chat | 14.99 | 16.23 | 15.00 | 16.22 | 15.61 |
Claude-instant-1 | 12.52 | 17.13 | 15.10 | 13.57 | 14.58 |
FlagAlpha/Atom-7B | 5.60 | 13.57 | 7.71 | 11.84 | 9.68 |
Results via ievals ( settings : 0-shot direct answering )
Citation
@article{ikala2023eval,
title={An Improved Traditional Chinese Evaluation Suite for Foundation Model},
author={Tam, Zhi-Rui and Pai, Ya-Ting},
journal={arXiv},
year={2023}
}
CONTENT WARNING This is a modification of ikala/tmmluplus, with minor alterations made to facilitate the implementation for lm-evaluation-harness purposes. More details on Discussions
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