Datasets:

ikala
/

tmmluplus

	---
	license: mit
	license_name: mit
	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
	configs:
	- config_name: engineering_math
	data_files:
	- split: train
	path: "data/engineering_math_dev.csv"
	- split: validation
	path: "data/engineering_math_val.csv"
	- split: test
	path: "data/engineering_math_test.csv"
	- config_name: dentistry
	data_files:
	- split: train
	path: "data/dentistry_dev.csv"
	- split: validation
	path: "data/dentistry_val.csv"
	- split: test
	path: "data/dentistry_test.csv"
	- config_name: traditional_chinese_medicine_clinical_medicine
	data_files:
	- split: train
	path: "data/traditional_chinese_medicine_clinical_medicine_dev.csv"
	- split: validation
	path: "data/traditional_chinese_medicine_clinical_medicine_val.csv"
	- split: test
	path: "data/traditional_chinese_medicine_clinical_medicine_test.csv"
	- config_name: clinical_psychology
	data_files:
	- split: train
	path: "data/clinical_psychology_dev.csv"
	- split: validation
	path: "data/clinical_psychology_val.csv"
	- split: test
	path: "data/clinical_psychology_test.csv"
	- config_name: technical
	data_files:
	- split: train
	path: "data/technical_dev.csv"
	- split: validation
	path: "data/technical_val.csv"
	- split: test
	path: "data/technical_test.csv"
	- config_name: culinary_skills
	data_files:
	- split: train
	path: "data/culinary_skills_dev.csv"
	- split: validation
	path: "data/culinary_skills_val.csv"
	- split: test
	path: "data/culinary_skills_test.csv"
	- config_name: mechanical
	data_files:
	- split: train
	path: "data/mechanical_dev.csv"
	- split: validation
	path: "data/mechanical_val.csv"
	- split: test
	path: "data/mechanical_test.csv"
	- config_name: logic_reasoning
	data_files:
	- split: train
	path: "data/logic_reasoning_dev.csv"
	- split: validation
	path: "data/logic_reasoning_val.csv"
	- split: test
	path: "data/logic_reasoning_test.csv"
	- config_name: real_estate
	data_files:
	- split: train
	path: "data/real_estate_dev.csv"
	- split: validation
	path: "data/real_estate_val.csv"
	- split: test
	path: "data/real_estate_test.csv"
	- config_name: general_principles_of_law
	data_files:
	- split: train
	path: "data/general_principles_of_law_dev.csv"
	- split: validation
	path: "data/general_principles_of_law_val.csv"
	- split: test
	path: "data/general_principles_of_law_test.csv"
	- config_name: finance_banking
	data_files:
	- split: train
	path: "data/finance_banking_dev.csv"
	- split: validation
	path: "data/finance_banking_val.csv"
	- split: test
	path: "data/finance_banking_test.csv"
	- config_name: anti_money_laundering
	data_files:
	- split: train
	path: "data/anti_money_laundering_dev.csv"
	- split: validation
	path: "data/anti_money_laundering_val.csv"
	- split: test
	path: "data/anti_money_laundering_test.csv"
	- config_name: ttqav2
	data_files:
	- split: train
	path: "data/ttqav2_dev.csv"
	- split: validation
	path: "data/ttqav2_val.csv"
	- split: test
	path: "data/ttqav2_test.csv"
	- config_name: marketing_management
	data_files:
	- split: train
	path: "data/marketing_management_dev.csv"
	- split: validation
	path: "data/marketing_management_val.csv"
	- split: test
	path: "data/marketing_management_test.csv"
	- config_name: business_management
	data_files:
	- split: train
	path: "data/business_management_dev.csv"
	- split: validation
	path: "data/business_management_val.csv"
	- split: test
	path: "data/business_management_test.csv"
	- config_name: organic_chemistry
	data_files:
	- split: train
	path: "data/organic_chemistry_dev.csv"
	- split: validation
	path: "data/organic_chemistry_val.csv"
	- split: test
	path: "data/organic_chemistry_test.csv"
	- config_name: advance_chemistry
	data_files:
	- split: train
	path: "data/advance_chemistry_dev.csv"
	- split: validation
	path: "data/advance_chemistry_val.csv"
	- split: test
	path: "data/advance_chemistry_test.csv"
	- config_name: physics
	data_files:
	- split: train
	path: "data/physics_dev.csv"
	- split: validation
	path: "data/physics_val.csv"
	- split: test
	path: "data/physics_test.csv"
	- config_name: secondary_physics
	data_files:
	- split: train
	path: "data/secondary_physics_dev.csv"
	- split: validation
	path: "data/secondary_physics_val.csv"
	- split: test
	path: "data/secondary_physics_test.csv"
	- config_name: human_behavior
	data_files:
	- split: train
	path: "data/human_behavior_dev.csv"
	- split: validation
	path: "data/human_behavior_val.csv"
	- split: test
	path: "data/human_behavior_test.csv"
	- config_name: national_protection
	data_files:
	- split: train
	path: "data/national_protection_dev.csv"
	- split: validation
	path: "data/national_protection_val.csv"
	- split: test
	path: "data/national_protection_test.csv"
	- config_name: jce_humanities
	data_files:
	- split: train
	path: "data/jce_humanities_dev.csv"
	- split: validation
	path: "data/jce_humanities_val.csv"
	- split: test
	path: "data/jce_humanities_test.csv"
	- config_name: politic_science
	data_files:
	- split: train
	path: "data/politic_science_dev.csv"
	- split: validation
	path: "data/politic_science_val.csv"
	- split: test
	path: "data/politic_science_test.csv"
	- config_name: agriculture
	data_files:
	- split: train
	path: "data/agriculture_dev.csv"
	- split: validation
	path: "data/agriculture_val.csv"
	- split: test
	path: "data/agriculture_test.csv"
	- config_name: official_document_management
	data_files:
	- split: train
	path: "data/official_document_management_dev.csv"
	- split: validation
	path: "data/official_document_management_val.csv"
	- split: test
	path: "data/official_document_management_test.csv"
	- config_name: financial_analysis
	data_files:
	- split: train
	path: "data/financial_analysis_dev.csv"
	- split: validation
	path: "data/financial_analysis_val.csv"
	- split: test
	path: "data/financial_analysis_test.csv"
	- config_name: pharmacy
	data_files:
	- split: train
	path: "data/pharmacy_dev.csv"
	- split: validation
	path: "data/pharmacy_val.csv"
	- split: test
	path: "data/pharmacy_test.csv"
	- config_name: educational_psychology
	data_files:
	- split: train
	path: "data/educational_psychology_dev.csv"
	- split: validation
	path: "data/educational_psychology_val.csv"
	- split: test
	path: "data/educational_psychology_test.csv"
	- config_name: statistics_and_machine_learning
	data_files:
	- split: train
	path: "data/statistics_and_machine_learning_dev.csv"
	- split: validation
	path: "data/statistics_and_machine_learning_val.csv"
	- split: test
	path: "data/statistics_and_machine_learning_test.csv"
	- config_name: management_accounting
	data_files:
	- split: train
	path: "data/management_accounting_dev.csv"
	- split: validation
	path: "data/management_accounting_val.csv"
	- split: test
	path: "data/management_accounting_test.csv"
	- config_name: introduction_to_law
	data_files:
	- split: train
	path: "data/introduction_to_law_dev.csv"
	- split: validation
	path: "data/introduction_to_law_val.csv"
	- split: test
	path: "data/introduction_to_law_test.csv"
	- config_name: computer_science
	data_files:
	- split: train
	path: "data/computer_science_dev.csv"
	- split: validation
	path: "data/computer_science_val.csv"
	- split: test
	path: "data/computer_science_test.csv"
	- config_name: veterinary_pathology
	data_files:
	- split: train
	path: "data/veterinary_pathology_dev.csv"
	- split: validation
	path: "data/veterinary_pathology_val.csv"
	- split: test
	path: "data/veterinary_pathology_test.csv"
	- config_name: accounting
	data_files:
	- split: train
	path: "data/accounting_dev.csv"
	- split: validation
	path: "data/accounting_val.csv"
	- split: test
	path: "data/accounting_test.csv"
	- config_name: fire_science
	data_files:
	- split: train
	path: "data/fire_science_dev.csv"
	- split: validation
	path: "data/fire_science_val.csv"
	- split: test
	path: "data/fire_science_test.csv"
	- config_name: optometry
	data_files:
	- split: train
	path: "data/optometry_dev.csv"
	- split: validation
	path: "data/optometry_val.csv"
	- split: test
	path: "data/optometry_test.csv"
	- config_name: insurance_studies
	data_files:
	- split: train
	path: "data/insurance_studies_dev.csv"
	- split: validation
	path: "data/insurance_studies_val.csv"
	- split: test
	path: "data/insurance_studies_test.csv"
	- config_name: pharmacology
	data_files:
	- split: train
	path: "data/pharmacology_dev.csv"
	- split: validation
	path: "data/pharmacology_val.csv"
	- split: test
	path: "data/pharmacology_test.csv"
	- config_name: taxation
	data_files:
	- split: train
	path: "data/taxation_dev.csv"
	- split: validation
	path: "data/taxation_val.csv"
	- split: test
	path: "data/taxation_test.csv"
	- config_name: trust_practice
	data_files:
	- split: train
	path: "data/trust_practice_dev.csv"
	- split: validation
	path: "data/trust_practice_val.csv"
	- split: test
	path: "data/trust_practice_test.csv"
	- config_name: geography_of_taiwan
	data_files:
	- split: train
	path: "data/geography_of_taiwan_dev.csv"
	- split: validation
	path: "data/geography_of_taiwan_val.csv"
	- split: test
	path: "data/geography_of_taiwan_test.csv"
	- config_name: physical_education
	data_files:
	- split: train
	path: "data/physical_education_dev.csv"
	- split: validation
	path: "data/physical_education_val.csv"
	- split: test
	path: "data/physical_education_test.csv"
	- config_name: auditing
	data_files:
	- split: train
	path: "data/auditing_dev.csv"
	- split: validation
	path: "data/auditing_val.csv"
	- split: test
	path: "data/auditing_test.csv"
	- config_name: administrative_law
	data_files:
	- split: train
	path: "data/administrative_law_dev.csv"
	- split: validation
	path: "data/administrative_law_val.csv"
	- split: test
	path: "data/administrative_law_test.csv"
	- config_name: education_(profession_level)
	data_files:
	- split: train
	path: "data/education_(profession_level)_dev.csv"
	- split: validation
	path: "data/education_(profession_level)_val.csv"
	- split: test
	path: "data/education_(profession_level)_test.csv"
	- config_name: economics
	data_files:
	- split: train
	path: "data/economics_dev.csv"
	- split: validation
	path: "data/economics_val.csv"
	- split: test
	path: "data/economics_test.csv"
	- config_name: veterinary_pharmacology
	data_files:
	- split: train
	path: "data/veterinary_pharmacology_dev.csv"
	- split: validation
	path: "data/veterinary_pharmacology_val.csv"
	- split: test
	path: "data/veterinary_pharmacology_test.csv"
	- config_name: nautical_science
	data_files:
	- split: train
	path: "data/nautical_science_dev.csv"
	- split: validation
	path: "data/nautical_science_val.csv"
	- split: test
	path: "data/nautical_science_test.csv"
	- config_name: occupational_therapy_for_psychological_disorders
	data_files:
	- split: train
	path: "data/occupational_therapy_for_psychological_disorders_dev.csv"
	- split: validation
	path: "data/occupational_therapy_for_psychological_disorders_val.csv"
	- split: test
	path: "data/occupational_therapy_for_psychological_disorders_test.csv"
	- config_name: basic_medical_science
	data_files:
	- split: train
	path: "data/basic_medical_science_dev.csv"
	- split: validation
	path: "data/basic_medical_science_val.csv"
	- split: test
	path: "data/basic_medical_science_test.csv"
	- config_name: macroeconomics
	data_files:
	- split: train
	path: "data/macroeconomics_dev.csv"
	- split: validation
	path: "data/macroeconomics_val.csv"
	- split: test
	path: "data/macroeconomics_test.csv"
	- config_name: trade
	data_files:
	- split: train
	path: "data/trade_dev.csv"
	- split: validation
	path: "data/trade_val.csv"
	- split: test
	path: "data/trade_test.csv"
	- config_name: chinese_language_and_literature
	data_files:
	- split: train
	path: "data/chinese_language_and_literature_dev.csv"
	- split: validation
	path: "data/chinese_language_and_literature_val.csv"
	- split: test
	path: "data/chinese_language_and_literature_test.csv"
	- config_name: tve_design
	data_files:
	- split: train
	path: "data/tve_design_dev.csv"
	- split: validation
	path: "data/tve_design_val.csv"
	- split: test
	path: "data/tve_design_test.csv"
	- config_name: junior_science_exam
	data_files:
	- split: train
	path: "data/junior_science_exam_dev.csv"
	- split: validation
	path: "data/junior_science_exam_val.csv"
	- split: test
	path: "data/junior_science_exam_test.csv"
	- config_name: junior_math_exam
	data_files:
	- split: train
	path: "data/junior_math_exam_dev.csv"
	- split: validation
	path: "data/junior_math_exam_val.csv"
	- split: test
	path: "data/junior_math_exam_test.csv"
	- config_name: junior_chinese_exam
	data_files:
	- split: train
	path: "data/junior_chinese_exam_dev.csv"
	- split: validation
	path: "data/junior_chinese_exam_val.csv"
	- split: test
	path: "data/junior_chinese_exam_test.csv"
	- config_name: junior_social_studies
	data_files:
	- split: train
	path: "data/junior_social_studies_dev.csv"
	- split: validation
	path: "data/junior_social_studies_val.csv"
	- split: test
	path: "data/junior_social_studies_test.csv"
	- config_name: tve_mathematics
	data_files:
	- split: train
	path: "data/tve_mathematics_dev.csv"
	- split: validation
	path: "data/tve_mathematics_val.csv"
	- split: test
	path: "data/tve_mathematics_test.csv"
	- config_name: tve_chinese_language
	data_files:
	- split: train
	path: "data/tve_chinese_language_dev.csv"
	- split: validation
	path: "data/tve_chinese_language_val.csv"
	- split: test
	path: "data/tve_chinese_language_test.csv"
	- config_name: tve_natural_sciences
	data_files:
	- split: train
	path: "data/tve_natural_sciences_dev.csv"
	- split: validation
	path: "data/tve_natural_sciences_val.csv"
	- split: test
	path: "data/tve_natural_sciences_test.csv"
	- config_name: junior_chemistry
	data_files:
	- split: train
	path: "data/junior_chemistry_dev.csv"
	- split: validation
	path: "data/junior_chemistry_val.csv"
	- split: test
	path: "data/junior_chemistry_test.csv"
	- config_name: music
	data_files:
	- split: train
	path: "data/music_dev.csv"
	- split: validation
	path: "data/music_val.csv"
	- split: test
	path: "data/music_test.csv"
	- config_name: education
	data_files:
	- split: train
	path: "data/education_dev.csv"
	- split: validation
	path: "data/education_val.csv"
	- split: test
	path: "data/education_test.csv"
	- config_name: three_principles_of_people
	data_files:
	- split: train
	path: "data/three_principles_of_people_dev.csv"
	- split: validation
	path: "data/three_principles_of_people_val.csv"
	- split: test
	path: "data/three_principles_of_people_test.csv"
	- config_name: taiwanese_hokkien
	data_files:
	- split: train
	path: "data/taiwanese_hokkien_dev.csv"
	- split: validation
	path: "data/taiwanese_hokkien_val.csv"
	- split: test
	path: "data/taiwanese_hokkien_test.csv"
	---
	# TMMLU+ : Large scale traditional chinese massive multitask language understanding

	<p align="center">
	<img src="https://huggingface.co/datasets/ikala/tmmluplus/resolve/main/cover.png" alt="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/" style="max-width: 400" width=400 />
	</p>
	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](https://github.com/mtkresearch/MR-Models/tree/main/TC-Eval/data/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.


	```python
	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']
	```

	For each dataset split

	```python
	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 \|
	\|------------\|------------\|------------\|------------\|------------\|------------\|
	\|Gemini-1.5-pro \| 66.18\|70.29\|61.84\|60.30\|64.65\|
	\| [Qwen/Qwen-72B](https://huggingface.co/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-max \| 59.92 \| 66.95 \| 57.43 \| 56.48 \|60.20\|
	\| [Qwen/Qwen-72B-Chat](https://huggingface.co/Qwen/Qwen-72B-Chat) \| 55.15 \| 66.20 \| 55.65 \| 57.19 \|58.55\|
	\| [Qwen/Qwen-14B](https://huggingface.co/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](https://huggingface.co/01-ai/Yi-34B-Chat) \| 40.24 \| 56.77 \| 53.99 \| 47.58 \|49.64\|
	\| Gemini-1.5-flash \|53.47\|53.42\|42.99\|46.56\|49.11\|
	\| [Reka Flash](https://www.reka.ai/)\|45.26\|52.91\|46.31\|43.76\|47.06\|
	\| [Qwen/Qwen-14B-Chat](https://huggingface.co/Qwen/Qwen-14B-Chat) \| 43.86 \| 53.29 \| 44.78 \| 45.13 \|46.77\|
	\| [Qwen/Qwen1.5-14B-Chat](https://huggingface.co/Qwen/Qwen1.5-14B-Chat)\|39.65\|52.76\|43.90\|44.95\|45.31\|
	\| [01-ai/Yi-6B-Chat](https://huggingface.co/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\|
	\| [MediaTek-Research/Breeze-7B-Instruct-v0_1](https://huggingface.co/MediaTek-Research/Breeze-7B-Instruct-v0_1)\| 36.46 \| 48.38 \|45.11 \|40.75 \| 42.67 \|
	\| gpt-3.5-turbo-0613 \| 41.56 \| 46.72 \| 36.73 \| 42.03 \|41.76\|
	\| [CausalLM/14B](https://huggingface.co/CausalLM/14B) \| 39.83 \| 44.50 \| 39.61 \| 41.97 \|41.48\|
	\| [Skywork/Skywork-13B-base](https://huggingface.co/Skywork/Skywork-13B-base) \| 36.93 \| 47.27 \| 41.04 \| 40.10 \|41.33\|
	\| Claude-3-opus \|42.95\|45.49\|35.79\|40.24\|41.12\|
	\| [Qwen/Qwen-7B](https://huggingface.co/Qwen/Qwen-7B) \| 37.53 \| 45.48 \| 38.09 \| 38.96 \|40.01\|
	\| [meta-llama/Llama-3-70b-chat-hf](https://docs.together.ai/docs/inference-models) \| 34.44 \| 47.02 \| 37.50 \|39.51 \| 39.62 \|
	\| [Qwen/Qwen-7B-Chat](https://huggingface.co/Qwen/Qwen-7B-Chat) \| 33.32 \| 44.64 \| 40.27 \| 39.89 \|39.53\|
	\| [vivo-ai/BlueLM-7B-Base](https://huggingface.co/vivo-ai/BlueLM-7B-Base) \| 33.94 \| 41.52 \| 37.38 \| 38.74 \|37.90\|
	\| [baichuan-inc/Baichuan2-13B-Chat](https://huggingface.co/baichuan-inc/Baichuan2-13B-Chat) \| 29.64 \| 43.73 \| 37.36 \| 39.88 \|37.65\|
	\| [Qwen/Qwen-1_8B](https://huggingface.co/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](https://huggingface.co/THUDM/chatglm3-6b) \| 31.05 \| 39.31 \| 35.64 \| 35.60 \|35.40\|
	\| [deepseek-ai/deepseek-llm-7b-chat](https://huggingface.co/deepseek-ai/deepseek-llm-7b-chat) \| 29.82 \| 42.29 \| 34.24 \| 34.31 \|35.17\|
	\| [CausalLM/7B](https://huggingface.co/CausalLM/7B) \| 31.03 \| 38.17 \| 35.87 \| 35.39 \|35.11\|
	\| [Azure99/blossom-v3_1-mistral-7b](https://huggingface.co/Azure99/blossom-v3_1-mistral-7b) \| 32.80 \| 36.91 \| 32.36 \| 34.53 \|34.15\|
	\| [google/gemma-7b-it](https://huggingface.co/google/gemma-7b-it) \| 31.89 \| 35.70 \| 34.00 \| 33.79 \| 33.84 \|
	\| [Reka Edge](https://www.reka.ai/)\|30.02\|39.40\|31.84\|32.36\|33.41\|
	\| [microsoft/Orca-2-13b](https://huggingface.co/microsoft/Orca-2-13b) \| 24.69 \| 39.18 \| 33.60 \| 31.99 \|32.37\|
	\| [Qwen/Qwen-1_8B-Chat](https://huggingface.co/Qwen/Qwen-1_8B-Chat) \| 26.60 \| 36.36 \| 31.81 \| 31.96 \|31.68\|
	\| [meta-llama/Llama-3-8b-chat-hf](https://docs.together.ai/docs/inference-models) \| 31.52 \| 34.19 \| 28.91 \| 31.79 \| 31.60 \|
	\| [TigerResearch/tigerbot-13b-chat-v3](https://huggingface.co/TigerResearch/tigerbot-13b-chat-v3) \| 24.73 \| 29.63 \| 25.72 \| 27.22 \|26.82\|
	\| [hongyin/mistral-7b-80k](https://huggingface.co/hongyin/mistral-7b-80k) \| 24.26 \| 23.76 \| 22.56 \| 24.57 \|23.79\|
	\| [deepseek-ai/deepseek-llm-67b-chat](https://huggingface.co/deepseek-ai/deepseek-llm-67b-chat) \| 19.10 \| 26.06 \| 21.51 \| 21.77 \|22.11\|
	\| [yentinglin/Taiwan-LLM-13B-v2.0-chat](https://huggingface.co/yentinglin/Taiwan-LLM-13B-v2.0-chat) \| 18.53 \| 27.65 \| 17.77 \| 21.49 \|21.36\|
	\| [GeneZC/MiniChat-3B](https://huggingface.co/GeneZC/MiniChat-3B) \| 17.66 \| 23.35 \| 22.71 \| 20.34 \|21.02\|
	\| [LinkSoul/Chinese-Llama-2-7b](https://huggingface.co/LinkSoul/Chinese-Llama-2-7b) \| 16.55 \| 18.39 \| 12.97 \| 16.13 \|16.01\|
	\| [yentinglin/Taiwan-LLM-7B-v2.1-chat](https://huggingface.co/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](https://huggingface.co/FlagAlpha/Atom-7B) \| 5.60 \| 13.57 \| 7.71 \| 11.84 \|9.68\|

	Results via [ievals](https://github.com/iKala/ievals) ( settings : 0-shot direct answering )


	# Citation

	```
	@article{ikala2024improved,
	title={An Improved Traditional Chinese Evaluation Suite for Foundation Model},
	author={Tam, Zhi-Rui and Pai, Ya-Ting and Lee, Yen-Wei and Cheng, Sega and Shuai, Hong-Han},
	journal={arXiv preprint arXiv:2403.01858},
	year={2024}
	}
	```