MMMLU

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+---
+task_categories:
+- question-answering
+configs:
+- config_name: default
+  data_files:
+  - split: test
+    path: test/*.csv
+- config_name: AR_XY
+  data_files:
+  - split: test
+    path: test/mmlu_AR-XY.csv
+- config_name: BN_BD
+  data_files:
+  - split: test
+    path: test/mmlu_BN-BD.csv
+- config_name: DE_DE
+  data_files:
+  - split: test
+    path: test/mmlu_DE-DE.csv
+- config_name: ES_LA
+  data_files:
+  - split: test
+    path: test/mmlu_ES-LA.csv
+- config_name: FR_FR
+  data_files:
+  - split: test
+    path: test/mmlu_FR-FR.csv
+- config_name: HI_IN
+  data_files:
+  - split: test
+    path: test/mmlu_HI-IN.csv
+- config_name: ID_ID
+  data_files:
+  - split: test
+    path: test/mmlu_ID-ID.csv
+- config_name: IT_IT
+  data_files:
+  - split: test
+    path: test/mmlu_IT-IT.csv
+- config_name: JA_JP
+  data_files:
+  - split: test
+    path: test/mmlu_JA-JP.csv
+- config_name: KO_KR
+  data_files:
+  - split: test
+    path: test/mmlu_KO-KR.csv
+- config_name: PT_BR
+  data_files:
+  - split: test
+    path: test/mmlu_PT-BR.csv
+- config_name: SW_KE
+  data_files:
+  - split: test
+    path: test/mmlu_SW-KE.csv
+- config_name: YO_NG
+  data_files:
+  - split: test
+    path: test/mmlu_YO-NG.csv
+- config_name: ZH_CN
+  data_files:
+  - split: test
+    path: test/mmlu_ZH-CN.csv
+language:
+- ar
+- bn
+- de
+- es
+- fr
+- hi
+- id
+- it
+- ja
+- ko
+- pt
+- sw
+- yo
+- zh
+license: mit
+---
+# Multilingual Massive Multitask Language Understanding (MMMLU)
+The MMLU is a widely recognized benchmark of general knowledge attained by AI models. It covers a broad range of topics from 57 different categories, covering elementary-level knowledge up to advanced professional subjects like law, physics, history, and computer science.
+We translated the MMLU’s test set into 14 languages using professional human translators. Relying on human translators for this evaluation increases confidence in the accuracy of the translations, especially for low-resource languages like Yoruba. We are publishing the professional human translations and the code we use to run the evaluations.
+This effort reflects our commitment to improving the multilingual capabilities of AI models, ensuring they perform accurately across languages, particularly for underrepresented communities. By prioritizing high-quality translations, we aim to make AI technology more inclusive and effective for users worldwide.
+## Locales
+MMMLU contains the MMLU test set translated into the following locales:
+* AR_XY (Arabic)
+* BN_BD (Bengali)
+* DE_DE (German)
+* ES_LA (Spanish)
+* FR_FR (French)
+* HI_IN (Hindi)
+* ID_ID (Indonesian)
+* IT_IT (Italian)
+* JA_JP (Japanese)
+* KO_KR (Korean)
+* PT_BR (Brazilian Portuguese)
+* SW_KE (Swahili)
+* YO_NG (Yoruba)
+* ZH_CN (Simplified Chinese)
+## Sources
+Hendrycks, D., Burns, C., Kadavath, S., Arora, A., Basart, S., Tang, E., Song, D., & Steinhardt, J. (2021). [*Measuring Massive Multitask Language Understanding*](https://arxiv.org/abs/2009.03300).
+[OpenAI Simple Evals GitHub Repository](https://github.com/openai/simple-evals)

test/mmlu_AR-XY.csv ADDED Viewed