BLEnD

This is the official repository of BLEnD: A Benchmark for LLMs on Everyday Knowledge in Diverse Cultures and Languages (Submitted to NeurIPS 2024 Datasets and Benchmarks Track).

24/12/05: Updated translation errors
25/05/02: Updated multiple choice questions file (v1.1)
26/09/15: Added new data collected for SemEval-2026 Task 7, covering 17 additional language-culture pairs (semeval-annotations, semeval-questions, and semeval split of multiple-choice-questions)

About

Large language models (LLMs) often lack culture-specific everyday knowledge, especially across diverse regions and non-English languages. Existing benchmarks for evaluating LLMs' cultural sensitivities are usually limited to a single language or online sources like Wikipedia, which may not reflect the daily habits, customs, and lifestyles of different regions. That is, information about the food people eat for their birthday celebrations, spices they typically use, musical instruments youngsters play, or the sports they practice in school is not always explicitly written online. To address this issue, we introduce BLEnD, a hand-crafted benchmark designed to evaluate LLMs' everyday knowledge across diverse cultures and languages. The benchmark comprises 52.6k question-answer pairs from 16 countries/regions, in 13 different languages, including low-resource ones such as Amharic, Assamese, Azerbaijani, Hausa, and Sundanese. We evaluate LLMs in two formats: short-answer questions, and multiple-choice questions. We show that LLMs perform better in cultures that are more present online, with a maximum 57.34% difference in GPT-4, the best-performing model, in the short-answer format. Furthermore, we find that LLMs perform better in their local languages for mid-to-high-resource languages. Interestingly, for languages deemed to be low-resource, LLMs provide better answers in English.

Requirements

datasets == 2.19.2
pandas == 2.1.4

Dataset

All the data samples for short-answer questions, including the human-annotated answers, can be found in the data/ directory. Specifically, the annotations from each country are included in the annotations split, and each country/region's data can be accessed by country codes.

from datasets import load_dataset

# Login using e.g. `huggingface-cli login` to access this dataset
ds = load_dataset("uilab/BLEnD", "short-answer-questions")

# To access data from Assam:
ds_as = ds['AS']

Each file includes a JSON variable with question IDs, questions in the local language and English, the human annotations both in the local language and English, and their respective vote counts as values. The same dataset for South Korea is shown below:

[{
    "ID": "Al-en-06",
    "question": "대한민국 학교 급식에서 흔히 볼 수 있는 음식은 무엇인가요?",
    "en_question": "What is a common school cafeteria food in your country?",
    "annotations": [
        {
            "answers": [
                "김치"
            ],
            "en_answers": [
                "kimchi"
            ],
            "count": 4
        },
        {
            "answers": [
                "밥",
                "쌀밥",
                "쌀"
            ],
            "en_answers": [
                "rice"
            ],
            "count": 3
        },
        ...
    ],
    "idks": {
        "idk": 0,
        "no-answer": 0,
        "not-applicable": 0
    }
}],

The topics and source language for each question can be found in short-answer-questions split. Questions for each country in their local languages and English can be accessed by country codes. Each CSV file question ID, topic, source language, question in English, and the local language (in the Translation column) for all questions.

from datasets import load_dataset

questions = load_dataset("uilab/BLEnD",'short-answer-questions')

# To access data from Assam:
assam_questions = questions['AS']

The current set of multiple choice questions and their answers can be found at the multiple-choice-questions split.

from datasets import load_dataset

mcq = load_dataset("uilab/BLEnD",'multiple-choice-questions')

Country/Region Codes

Country/Region	Code	Language	Code
United States	US	English	en
United Kingdom	GB	English	en
China	CN	Chinese	zh
Spain	ES	Spanish	es
Mexico	MX	Spanish	es
Indonesia	ID	Indonesian	id
South Korea	KR	Korean	ko
North Korea	KP	Korean	ko
Greece	GR	Greek	el
Iran	IR	Persian	fa
Algeria	DZ	Arabic	ar
Azerbaijan	AZ	Azerbaijani	az
West Java	JB	Sundanese	su
Assam	AS	Assamese	as
Northern Nigeria	NG	Hausa	ha
Ethiopia	ET	Amharic	am

SemEval-2026 Task 7 Data

As part of SemEval-2026 Task 7, we collected the same type of data for 17 additional language-culture pairs, expanding BLEnD's original 13 languages and 16 cultures. The newly added language-culture pairs are as follows:

Language-Culture Pair Codes

Country/Region	Code	Language	Code
Egypt	Arabic_Egypt	Arabic	ar
Morocco	Arabic_Morocco	Arabic	ar
Saudi Arabia	Arabic_SaudiArabia	Arabic	ar
Basque Country	Basque_BasqueCountry	Basque	eu
Bulgaria	Bulgarian_Bulgaria	Bulgarian	bg
Australia	English_Australia	English	en
France	French_France	French	fr
Ireland	Irish_Ireland	Irish	ga
Japan	Japanese_Japan	Japanese	ja
Singapore	Malay_Singapore	Malay	ms
Singapore	Mandarin_Singapore	Mandarin	zh
Singapore	Tamil_Singapore	Tamil	ta
Taiwan	Mandarin_Taiwan	Mandarin	zh
Ecuador	Spanish_Ecuador	Spanish	es
Sweden	Swedish_Sweden	Swedish	sv
Philippines	Tagalog_Philippines	Tagalog	tl
Sri Lanka	Tamil_SriLanka	Tamil	ta

This data follows the same format described above. Annotations and questions are available via the semeval-annotations and semeval-questions configs, with one split per language-culture pair (e.g. Arabic_Egypt, Mandarin_Taiwan). The corresponding multiple-choice questions are available in the semeval split of the multiple-choice-questions config.

from datasets import load_dataset

# Annotations
semeval_annotations = load_dataset("uilab/BLEnD", "semeval-annotations")
egypt_annotations = semeval_annotations["Arabic_Egypt"]

# Questions
semeval_questions = load_dataset("uilab/BLEnD", "semeval-questions")
egypt_questions = semeval_questions["Arabic_Egypt"]

# Multiple-choice questions
mcq = load_dataset("uilab/BLEnD", "multiple-choice-questions")
semeval_mcq = mcq["semeval"]

Citation

If you use BLEnD, please cite our paper:

@inproceedings{NEURIPS2024_8eb88844,
 author = {Myung, Junho and Lee, Nayeon and Zhou, Yi and Jin, Jiho and Putri, Rifki Afina and Antypas, Dimosthenis and Borkakoty, Hsuvas and Kim, Eunsu and Perez-Almendros, Carla and Ayele, Abinew Ali and Guti\'{e}rrez-Basulto, V\'{\i}ctor and Ib\'{a}\~{n}ez-Garc\'{\i}a, Yazm\'{\i}n and Lee, Hwaran and Muhammad, Shamsuddeen Hassan and Park, Kiwoong and Rzayev, Anar Sabuhi and White, Nina and Yimam, Seid Muhie and Pilehvar, Mohammad Taher and Ousidhoum, Nedjma and Camacho-Collados, Jose and Oh, Alice},
 booktitle = {Advances in Neural Information Processing Systems},
 doi = {10.52202/079017-2483},
 editor = {A. Globerson and L. Mackey and D. Belgrave and A. Fan and U. Paquet and J. Tomczak and C. Zhang},
 pages = {78104--78146},
 publisher = {Curran Associates, Inc.},
 title = {BLEnD: A Benchmark for LLMs on Everyday Knowledge in Diverse Cultures and Languages},
 url = {https://proceedings.neurips.cc/paper_files/paper/2024/file/8eb88844dafefa92a26aaec9f3acad93-Paper-Datasets_and_Benchmarks_Track.pdf},
 volume = {37},
 year = {2024}
}

If you use the SemEval-2026 Task 7 data (semeval-annotations, semeval-questions, and the semeval split of multiple-choice-questions), please also cite:

@inproceedings{ousidhoum-etal-2026-semeval,
    title = "{S}em{E}val-2026 Task 7: Everyday Knowledge Across Diverse Languages and Cultures",
    author = "Ousidhoum, Nedjma  and
      Myung, Junho  and
      Perez-Almendros, Carla  and
      Jin, Jiho  and
      Keleg, Amr  and
      Beloucif, Meriem  and
      Zhou, Yi  and
      Agerri, Rodrigo  and
      Araujo, Vladimir  and
      Baes, Naomi  and
      Barry, James  and
      Boisson, Joanne  and
      Chen, Nancy F.  and
      de Kock, Christine  and
      Edwards, Aleksandra  and
      Fernandez de Landa, Joseba  and
      Fazli Imam, Mohamed  and
      Hakami, Huda  and
      Hsieh, Shu-Kai  and
      Imperial, Joseph Marvin  and
      Lee, Roy Ka-Wei  and
      Liu, Zhengyuan  and
      Lyu, Chenyang  and
      Samih, Younes  and
      Sjons, Johan  and
      Tan, Bryan  and
      Ushio, Asahi  and
      Zheng, Weihua  and
      Oh, Alice  and
      Camacho-Collados, Jose",
    editor = "Kochmar, Ekaterina  and
      Ghosh, Debanjan  and
      North, Kai  and
      Komachi, Mamoru  and
      Zampieri, Marcos",
    booktitle = "Proceedings of the 20th {I}nternational {W}orkshop on {S}emantic {E}valuation (2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2026.semeval-1.455/",
    doi = "10.18653/v1/2026.semeval-1.455",
    pages = "3823--3837",
    ISBN = "979-8-89176-414-9"
}