| --- |
| license: cc-by-sa-4.0 |
| task_categories: |
| - question-answering |
| language: |
| - en |
| - zh |
| - es |
| - id |
| - ko |
| - el |
| - fa |
| - ar |
| - az |
| - su |
| - as |
| - ha |
| - am |
| - eu |
| - bg |
| - fr |
| - ga |
| - ja |
| - ms |
| - sv |
| - tl |
| - ta |
| size_categories: |
| - 10K<n<100K |
| configs: |
| - config_name: annotations |
| data_files: |
| - split: DZ |
| path: "data/annotations_hf/Algeria_data.json" |
| - split: AS |
| path: "data/annotations_hf/Assam_data.json" |
| - split: AZ |
| path: "data/annotations_hf/Azerbaijan_data.json" |
| - split: CN |
| path: "data/annotations_hf/China_data.json" |
| - split: ET |
| path: "data/annotations_hf/Ethiopia_data.json" |
| - split: GR |
| path: "data/annotations_hf/Greece_data.json" |
| - split: ID |
| path: "data/annotations_hf/Indonesia_data.json" |
| - split: IR |
| path: "data/annotations_hf/Iran_data.json" |
| - split: MX |
| path: "data/annotations_hf/Mexico_data.json" |
| - split: KP |
| path: "data/annotations_hf/North_Korea_data.json" |
| - split: NG |
| path: "data/annotations_hf/Northern_Nigeria_data.json" |
| - split: KR |
| path: "data/annotations_hf/South_Korea_data.json" |
| - split: ES |
| path: "data/annotations_hf/Spain_data.json" |
| - split: GB |
| path: "data/annotations_hf/UK_data.json" |
| - split: US |
| path: "data/annotations_hf/US_data.json" |
| - split: JB |
| path: "data/annotations_hf/West_Java_data.json" |
| - config_name: short-answer-questions |
| data_files: |
| - split: DZ |
| path: "data/questions_hf/Algeria_questions.json" |
| - split: AS |
| path: "data/questions_hf/Assam_questions.json" |
| - split: AZ |
| path: "data/questions_hf/Azerbaijan_questions.json" |
| - split: CN |
| path: "data/questions_hf/China_questions.json" |
| - split: ET |
| path: "data/questions_hf/Ethiopia_questions.json" |
| - split: GR |
| path: "data/questions_hf/Greece_questions.json" |
| - split: ID |
| path: "data/questions_hf/Indonesia_questions.json" |
| - split: IR |
| path: "data/questions_hf/Iran_questions.json" |
| - split: MX |
| path: "data/questions_hf/Mexico_questions.json" |
| - split: KP |
| path: "data/questions_hf/North_Korea_questions.json" |
| - split: NG |
| path: "data/questions_hf/Northern_Nigeria_questions.json" |
| - split: KR |
| path: "data/questions_hf/South_Korea_questions.json" |
| - split: ES |
| path: "data/questions_hf/Spain_questions.json" |
| - split: GB |
| path: "data/questions_hf/UK_questions.json" |
| - split: US |
| path: "data/questions_hf/US_questions.json" |
| - split: JB |
| path: "data/questions_hf/West_Java_questions.json" |
| - config_name: multiple-choice-questions |
| data_files: |
| - split: test |
| path: "data/mc_questions_hf/mc_questions_file_v1.1.json" |
| - split: semeval |
| path: "data/mc_questions_hf/mc_questions_file_semeval.json" |
| - config_name: semeval-annotations |
| data_files: |
| - split: Arabic_Egypt |
| path: "data_SemEval/annotations_hf/Arabic_Egypt_data.json" |
| - split: Arabic_Morocco |
| path: "data_SemEval/annotations_hf/Arabic_Morocco_data.json" |
| - split: Arabic_SaudiArabia |
| path: "data_SemEval/annotations_hf/Arabic_SaudiArabia_data.json" |
| - split: Basque_BasqueCountry |
| path: "data_SemEval/annotations_hf/Basque_BasqueCountry_data.json" |
| - split: Bulgarian_Bulgaria |
| path: "data_SemEval/annotations_hf/Bulgarian_Bulgaria_data.json" |
| - split: English_Australia |
| path: "data_SemEval/annotations_hf/English_Australia_data.json" |
| - split: French_France |
| path: "data_SemEval/annotations_hf/French_France_data.json" |
| - split: Irish_Ireland |
| path: "data_SemEval/annotations_hf/Irish_Ireland_data.json" |
| - split: Japanese_Japan |
| path: "data_SemEval/annotations_hf/Japanese_Japan_data.json" |
| - split: Malay_Singapore |
| path: "data_SemEval/annotations_hf/Malay_Singapore_data.json" |
| - split: Mandarin_Singapore |
| path: "data_SemEval/annotations_hf/Mandarin_Singapore_data.json" |
| - split: Mandarin_Taiwan |
| path: "data_SemEval/annotations_hf/Mandarin_Taiwan_data.json" |
| - split: Spanish_Ecuador |
| path: "data_SemEval/annotations_hf/Spanish_Ecuador_data.json" |
| - split: Swedish_Sweden |
| path: "data_SemEval/annotations_hf/Swedish_Sweden_data.json" |
| - split: Tagalog_Philippines |
| path: "data_SemEval/annotations_hf/Tagalog_Philippines_data.json" |
| - split: Tamil_Singapore |
| path: "data_SemEval/annotations_hf/Tamil_Singapore_data.json" |
| - split: Tamil_SriLanka |
| path: "data_SemEval/annotations_hf/Tamil_SriLanka_data.json" |
| - config_name: semeval-questions |
| data_files: |
| - split: Arabic_Egypt |
| path: "data_SemEval/questions_hf/Arabic_Egypt_questions.json" |
| - split: Arabic_Morocco |
| path: "data_SemEval/questions_hf/Arabic_Morocco_questions.json" |
| - split: Arabic_SaudiArabia |
| path: "data_SemEval/questions_hf/Arabic_SaudiArabia_questions.json" |
| - split: Basque_BasqueCountry |
| path: "data_SemEval/questions_hf/Basque_BasqueCountry_questions.json" |
| - split: Bulgarian_Bulgaria |
| path: "data_SemEval/questions_hf/Bulgarian_Bulgaria_questions.json" |
| - split: English_Australia |
| path: "data_SemEval/questions_hf/English_Australia_questions.json" |
| - split: French_France |
| path: "data_SemEval/questions_hf/French_France_questions.json" |
| - split: Irish_Ireland |
| path: "data_SemEval/questions_hf/Irish_Ireland_questions.json" |
| - split: Japanese_Japan |
| path: "data_SemEval/questions_hf/Japanese_Japan_questions.json" |
| - split: Malay_Singapore |
| path: "data_SemEval/questions_hf/Malay_Singapore_questions.json" |
| - split: Mandarin_Singapore |
| path: "data_SemEval/questions_hf/Mandarin_Singapore_questions.json" |
| - split: Mandarin_Taiwan |
| path: "data_SemEval/questions_hf/Mandarin_Taiwan_questions.json" |
| - split: Spanish_Ecuador |
| path: "data_SemEval/questions_hf/Spanish_Ecuador_questions.json" |
| - split: Swedish_Sweden |
| path: "data_SemEval/questions_hf/Swedish_Sweden_questions.json" |
| - split: Tagalog_Philippines |
| path: "data_SemEval/questions_hf/Tagalog_Philippines_questions.json" |
| - split: Tamil_Singapore |
| path: "data_SemEval/questions_hf/Tamil_Singapore_questions.json" |
| - split: Tamil_SriLanka |
| path: "data_SemEval/questions_hf/Tamil_SriLanka_questions.json" |
| --- |
| # BLEnD |
|
|
| This is the official repository of **[BLEnD: A Benchmark for LLMs on Everyday Knowledge in Diverse Cultures and Languages](https://arxiv.org/abs/2406.09948)** (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](https://github.com/BLEnD-SemEval2026/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 |
| ```Python |
| 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](https://huggingface.co/datasets/uilab/BLEnD#countryregion-codes)**. |
| ```Python |
| 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: |
| ```JSON |
| [{ |
| "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](https://huggingface.co/datasets/uilab/BLEnD#countryregion-codes)**. |
| Each CSV file question ID, topic, source language, question in English, and the local language (in the `Translation` column) for all questions. |
| ```Python |
| 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. |
| ```Python |
| 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](https://github.com/BLEnD-SemEval2026/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. |
| ```Python |
| 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: |
| ```bibtex |
| @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: |
| ```bibtex |
| @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" |
| } |
| ``` |
| |
