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--- |
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license: other |
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license_name: creative-commons-by-nc |
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task_categories: |
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- question-answering |
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language: |
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- zh |
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tags: |
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- traditional chinese |
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- finance |
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- medical |
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- taiwan |
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- benchmark |
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- zh-tw |
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- zh-hant |
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pretty_name: tmmlu++ |
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size_categories: |
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- 100K<n<1M |
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--- |
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# TMMLU+ : Large scale traditional chinese massive multitask language understanding |
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<p align="center"> |
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<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 /> |
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</p> |
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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. |
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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-source 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. |
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```python |
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from datasets import load_dataset |
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task_list = [ |
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'engineering_math', 'dentistry', 'traditional_chinese_medicine_clinical_medicine', 'clinical_psychology', 'technical', 'culinary_skills', 'mechanical', 'logic_reasoning', 'real_estate', |
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'general_principles_of_law', 'finance_banking', 'anti_money_laundering', 'ttqav2', 'marketing_management', 'business_management', 'organic_chemistry', 'advance_chemistry', |
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'physics', 'secondary_physics', 'human_behavior', 'national_protection', 'jce_humanities', 'politic_science', 'agriculture', 'official_document_management', |
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'financial_analysis', 'pharmacy', 'educational_psychology', 'statistics_and_machine_learning', 'management_accounting', 'introduction_to_law', 'computer_science', 'veterinary_pathology', |
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'accounting', 'fire_science', 'optometry', 'insurance_studies', 'pharmacology', 'taxation', 'trust_practice', 'geography_of_taiwan', 'physical_education', 'auditing', 'administrative_law', |
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'education_(profession_level)', 'economics', 'veterinary_pharmacology', 'nautical_science', 'occupational_therapy_for_psychological_disorders', |
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'basic_medical_science', 'macroeconomics', 'trade', 'chinese_language_and_literature', 'tve_design', 'junior_science_exam', 'junior_math_exam', 'junior_chinese_exam', |
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'junior_social_studies', 'tve_mathematics', 'tve_chinese_language', 'tve_natural_sciences', 'junior_chemistry', 'music', 'education', 'three_principles_of_people', |
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'taiwanese_hokkien' |
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] |
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for task in task_list: |
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val = load_dataset('ikala/tmmluplus', task)['validation'] |
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dev = load_dataset('ikala/tmmluplus', task)['train'] |
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test = load_dataset('ikala/tmmluplus', task)['test'] |
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``` |
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Statistic on all four categories : STEM, Social Science, Humanities, Other |
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| Category | Test | Dev | Validation | |
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|----------------------------------|-------|------|------------| |
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| STEM | 3458 | 70 | 385 | |
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| Social Sciences | 5958 | 90 | 665 | |
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| Humanities | 1763 | 35 | 197 | |
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| Other (Business, Health, Misc.) | 8939 | 135 | 995 | |
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| **Total** | 20118 | 330 | 2242 | |
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## Benchmark on direct prompting |
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| model | STEM | Social Science | Humanities | Other | Average | |
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|------------|------------|------------|------------|------------|------------| |
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| [Qwen/Qwen-72B](https://huggingface.co/Qwen/Qwen-72B) | 61.12 | 71.65 | 63.00 | 61.31 |64.27| |
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| [Qwen/Qwen-14B](https://huggingface.co/Qwen/Qwen-14B) | 46.94 | 56.69 | 49.43 | 48.81 |50.47| |
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| Gemini-pro | 45.38 | 57.29 | 48.80 | 48.21 |49.92| |
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| [01-ai/Yi-34B-Chat](https://huggingface.co/01-ai/Yi-34B-Chat) | 40.24 | 56.77 | 53.99 | 47.58 |49.64| |
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| [Qwen/Qwen-14B-Chat](https://huggingface.co/Qwen/Qwen-14B-Chat) | 43.86 | 53.29 | 44.78 | 45.13 |46.77| |
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| [01-ai/Yi-6B-Chat](https://huggingface.co/01-ai/Yi-6B-Chat) | 39.62 | 50.24 | 44.44 | 44.26 |44.64| |
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| Claude-1.3 | 42.65 | 49.33 | 42.16 | 44.14 |44.57| |
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| gpt-3.5-turbo-0613 | 41.56 | 46.72 | 36.73 | 42.03 |41.76| |
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| [CausalLM/14B](https://huggingface.co/CausalLM/14B) | 39.83 | 44.50 | 39.61 | 41.97 |41.48| |
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| [Skywork/Skywork-13B-base](https://huggingface.co/Skywork/Skywork-13B-base) | 36.93 | 47.27 | 41.04 | 40.10 |41.33| |
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| [Qwen/Qwen-7B](https://huggingface.co/Qwen/Qwen-7B) | 37.53 | 45.48 | 38.09 | 38.96 |40.01| |
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| [Qwen/Qwen-7B-Chat](https://huggingface.co/Qwen/Qwen-7B-Chat) | 33.32 | 44.64 | 40.27 | 39.89 |39.53| |
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| [vivo-ai/BlueLM-7B-Base](https://huggingface.co/vivo-ai/BlueLM-7B-Base) | 33.94 | 41.52 | 37.38 | 38.74 |37.90| |
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| [baichuan-inc/Baichuan2-13B-Chat](https://huggingface.co/baichuan-inc/Baichuan2-13B-Chat) | 29.64 | 43.73 | 37.36 | 39.88 |37.65| |
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| [Qwen/Qwen-1_8B](https://huggingface.co/Qwen/Qwen-1_8B) | 32.65 | 38.95 | 38.34 | 35.27 |36.30| |
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| Claude-2 | 39.65 | 39.09 | 28.59 | 37.47 |36.20| |
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| [THUDM/chatglm3-6b](https://huggingface.co/THUDM/chatglm3-6b) | 31.05 | 39.31 | 35.64 | 35.60 |35.40| |
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| [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| |
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| [CausalLM/7B](https://huggingface.co/CausalLM/7B) | 31.03 | 38.17 | 35.87 | 35.39 |35.11| |
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| [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| |
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| [Qwen/Qwen-1_8B-Chat](https://huggingface.co/Qwen/Qwen-1_8B-Chat) | 26.60 | 36.36 | 31.81 | 31.96 |31.68| |
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| [TigerResearch/tigerbot-13b-chat-v3](https://huggingface.co/TigerResearch/tigerbot-13b-chat-v3) | 24.73 | 29.63 | 25.72 | 27.22 |26.82| |
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| [hongyin/mistral-7b-80k](https://huggingface.co/hongyin/mistral-7b-80k) | 24.26 | 23.76 | 22.56 | 24.57 |23.79| |
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| [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| |
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| [LinkSoul/Chinese-Llama-2-7b](https://huggingface.co/LinkSoul/Chinese-Llama-2-7b) | 16.55 | 18.39 | 12.97 | 16.13 |16.01| |
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| [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| |
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| [FlagAlpha/Atom-7B](https://huggingface.co/FlagAlpha/Atom-7B) | 5.60 | 13.57 | 7.71 | 11.84 |9.68| |
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