UD-Filipino/Global-MMLU-Filipino

This dataset is a direct copy of CohereForAI/Global-MMLU, but focuses on Filipino and split by subject for the purposes of FilBench. The text below is from the original HuggingFace dataset.

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Dataset Summary

Global-MMLU 🌍 is a multilingual evaluation set spanning 42 languages, including English. This dataset combines machine translations for MMLU questions along with professional translations and crowd-sourced post-edits. It also includes cultural sensitivity annotations for a subset of the questions (2850 questions per language) and classifies them as Culturally Sensitive (CS) 🗽 or Culturally Agnostic (CA) ⚖️. These annotations were collected as part of an open science initiative led by Cohere For AI in collaboration with many external collaborators from both industry and academia.

• Curated by: Professional annotators and contributors of Cohere For AI Community.
• Language(s): 42 languages.
• License: Apache 2.0

Note: We also provide a "lite" version of Global MMLU called "Global-MMLU-Lite". This datatset is more balanced containing 200 samples each for CA and CS subsets for each language. And provides coverage for 15 languages with human translations.

Global-MMLU Dataset Family:

Name	Explanation
Global-MMLU	Full Global-MMLU set with translations for all 14K samples including CS and CA subsets
Global-MMLU-Lite	Lite version of Global-MMLU with human translated samples in 15 languages and containing 200 samples each for CS and CA subsets per language.

Load with Datasets

To load this dataset with datasets, you'll first need to install it using pip install datasets and then use the following code:

from datasets import load_dataset

# load HF dataset
global_mmlu = load_dataset("CohereForAI/Global-MMLU", 'en')

# can also be used as pandas dataframe
global_mmlu.set_format("pandas")
global_mmlu_test = global_mmlu['test'][:]
global_mmlu_dev = global_mmlu['dev'][:]

additional details

The columns corresponding to annotations collected from our cultural bias study (i.e. 'required_knowledge', 'time_sensitive', 'reference', 'culture', 'region', 'country') contain a list of values representing annotations from different annotators. However, to avoid conversion issues to HF dataset, these columns are provided as string in the final dataset. You can convert these columns back to list of values for easier manipulation as follows:

import ast

# convert string values to list
global_mmlu_df['required_knowledge'] = global_mmlu_df['required_knowledge'].apply(lamda x: ast.literal_eval(x))

Data Fields

The data fields are the same among all splits. Brief description of each field is provided below.

data field description

• sample_id: A unique identifier for the question.
• subject: The main topic the question falls under.
• subject_category: The high-level category the subject falls under i.e. STEM/Humanities/Social Sciences/Medical/Business/Other.
• question: translated question from MMLU
• option_a: one of the possible option choices
• option_b: one of the possible option choices
• option_c: one of the possible option choices
• option_d: one of the possible option choices
• `answer': the correct answer (A/B/C/D)
• required_knowledge: annotator votes for knowledge needed to answer the question correctly. Possible values include: "cultural", "regional", "dialect" or "none"
• time_sensitive: annotator votes indicating if the question's answer is time-dependent. Possible values include: Yes/No
• reference: annotations for which part of the question contains cultural/regional/dialect references. The different items in the list are annotations from different annotators.
• culture: annotations for which culture does the question belong to. The different items in the list correspond to annotations from different annotators.
• region: Geographic region the question is relevant to. Each item in the list correspond to annotations from different annotators.
• country: Specific country the question pertains to. Each item in the list correspond to annotations from different annotators.
• cultural_sensitivity_label: Label to indicate if question is culturally sensitive (CS) or culturally agnostic (CA) based on annotator votes.
• is_annotated: True/False flag to indicate if sample contains any annotations from our cultural bias study.

Data Splits

The following are the splits of the data:

Split	No. of instances	Language Coverage
test	589,764	42
dev	11,970	42

Data Instances

An example from test set looks as follows:

{'sample_id': 'world_religions/test/170'
 'subject': 'world_religions',
 'subject_category': 'Humanities',
 'question': ' The numen of Augustus referred to which of the following characteristics?',
 'option_a': 'Divine power',
 'option_b': 'Sexual virility',
 'option_c': 'Military acumen',
 'option_d': 'Philosophical intellect',
 'answer': 'A',
 'required_knowledge': "['none', 'cultural', 'cultural', 'cultural']",
 'time_sensitive': "['No', 'No', 'No', 'No']",
 'reference': "['-', '-', {'end': 22, 'label': 'Cultural', 'score': None, 'start': 5}, {'end': 22, 'label': 'Cultural', 'score': None, 'start': 5}]",
 'culture': "['Western Culture', 'Western Culture', 'Western Culture']",
 'region': "['North America', 'Europe']",
 'country': "['Italy']",
 'cultural_sensitivity_label': 'CS',
 'is_annotated': True,
 }

Statistics

Annotation Types

The following is the breakdown of CS🗽, CA⚖️ and MA📝 samples in the final dataset.

Type of Annotation	Instances per language	No. of languages	Total instances
Culturally Sensitive 🗽	792	42	33,264
Culturally Agnostic ⚖️	2058	42	86,436
MMLU Annotated 📝	2850	42	119,700