metadata

license: other
license_name: creative-commons-by-nc
task_categories:
  - question-answering
language:
  - zh
tags:
  - traditional chinese
  - finance
  - medical
  - taiwan
  - benchmark
  - zh-tw
  - zh-hant
pretty_name: tmmlu++
size_categories:
  - 100K<n<1M

TMMLU+ : Large scale traditional chinese massive multitask language understanding

A close-up image of a neat paper note with a white background. The text 'TMMLU+' is written horizontally across the center of the note in bold, black. Join us to work in multimodal LLM : https://ikala.ai/recruit/

We present TMMLU+ a traditional Chinese massive multitask language understanding dataset. TMMLU+ is a multiple-choice question-answering dataset with 66 subjects from elementary to professional level.

TMMLU+ dataset is 6 times larger and contains more balanced subjects compared to the previous version, TMMLU. We included benchmark results in TMMLU+ from closed-source models and 20 open-weight Chinese large language models of parameters ranging from 1.8B to 72B. Benchmark results show Traditional Chinese variants still lag behind those trained on Simplified Chinese major 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'
            ]
for task in task_list:
  val = load_dataset('ikala/tmmluplus', task)['validation']
  dev = load_dataset('ikala/tmmluplus', task)['train']
  test = load_dataset('ikala/tmmluplus', task)['test']

Statistic on all four category : 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
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
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
yentinglin/Taiwan-LLM-13B-v2.0-chat	18.53	27.65	17.77	21.49	21.36
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
FlagAlpha/Atom-7B	5.60	13.57	7.71	11.84	9.68