Datasets:

Lossfunk
/

Multilingual_CulturalBench

+---
+language:
+- en
+- hi
+- zh
+- pt
+- sw
+- he
+license: apache-2.0
+task_categories:
+- multiple-choice
+- question-answering
+size_categories:
+- n<1K
+tags:
+- culture
+- multilingual
+- evaluation
+pretty_name: Multilingual CulturalBench
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: multilingual_cultural_bench.csv
+---
+# Multilingual CulturalBench (Translated Subset)
+This dataset is a multilingual extension of the [CulturalBench](https://huggingface.co/datasets/kellycyy/CulturalBench) dataset ("Easy" subset). It contains **787 samples** from the original benchmark, translated into five additional languages using **Gemini-2.5-Flash**.
+## Dataset Description
+The original CulturalBench is designed to assess the cultural capabilities of Large Language Models (LLMs). This version extends the "Easy" subset (multiple-choice questions) by providing translations for the questions and options, enabling multilingual cultural evaluation.
+**Note:** This is a partial subset (787 samples) of the original 1,230 "Easy" samples.
+### Languages
+The dataset includes parallel data in the following languages:
+- **English** (Original)
+- **Hindi**
+- **Chinese** (Simplified)
+- **Brazilian Portuguese** (`braz_port`)
+- **Swahili**
+- **Hebrew**
+### Statistics
+**Total Samples:** 787
+#### Questions by Country
+| Country | Count | Country | Count | Country | Count |
+| :--- | :--- | :--- | :--- | :--- | :--- |
+| China | 59 | Vietnam | 27 | United States | 20 |
+| South Africa | 58 | United Kingdom | 25 | Peru | 19 |
+| Japan | 53 | Brazil | 25 | Morocco | 17 |
+| India | 46 | Bangladesh | 25 | Saudi Arabia | 17 |
+| South Korea | 41 | Singapore | 23 | Australia | 15 |
+| Iran | 37 | Lebanon | 22 | Pakistan | 14 |
+| Hong Kong | 36 | Nigeria | 22 | France | 14 |
+| Argentina | 35 | Chile | 22 | Israel | 13 |
+| Germany | 32 | Taiwan | 22 | Canada | 7 |
+| | | Nepal | 21 | | |
+| | | Egypt | 20 | **Total** | **787** |
+## Dataset Structure
+The dataset is a CSV file (`multilingual_cultural_bench.csv`) with **33 columns**. Each row represents a single question with its translations and ground truth.
+### Fields
+| Field | Description |
+| :--- | :--- |
+| `question_idx` | Unique identifier from the original CulturalBench. |
+| `english_question` | The original question in English. |
+| `english_option_[a-d]` | The four options in English. |
+| `hindi_question` | Translated question in Hindi. |
+| `hindi_option_[a-d]` | Translated options in Hindi. |
+| `chinese_question` | Translated question in Chinese. |
+| `chinese_option_[a-d]` | Translated options in Chinese. |
+| `braz_port_question` | Translated question in Brazilian Portuguese. |
+| `braz_port_option_[a-d]` | Translated options in Brazilian Portuguese. |
+| `swahili_question` | Translated question in Swahili. |
+| `swahili_option_[a-d]` | Translated options in Swahili. |
+| `hebrew_question` | Translated question in Hebrew. |
+| `hebrew_option_[a-d]` | Translated options in Hebrew. |
+| `answer` | The correct option label (A, B, C, or D). |
+| `country` | The country/culture the question pertains to. |
+## Sample Questions
+### Sample 1: South Korea (Dining Etiquette)
+#### Question
+| Language | Question |
+| :--- | :--- |
+| **English** | In Korean dining etiquette, what is a common practice regarding drinks and paying for the meal? |
+| **Hindi** | कोरियाई भोजन शिष्टाचार में, पेय और भोजन के भुगतान के संबंध में एक सामान्य प्रथा क्या है? |
+| **Chinese** | 在韩国的餐桌礼仪中，关于饮料和买单，常见的做法是什么？ |
+| **Brazilian Portuguese** | Na etiqueta de refeições coreana, qual é uma prática comum em relação a bebidas e ao pagamento da refeição? |
+#### Options
+| Language | Option A | Option B | Option C | Option D |
+| :--- | :--- | :--- | :--- | :--- |
+| **English** | Everyone pays for their own meal and drinks. | Younger people pour drinks for the elders and the elders pay for the meal. | The person who is older always pays, regardless of who did the invitation | It's common to split the bill evenly amongst all diners, regardless of age. |
+| **Hindi** | हर कोई अपने भोजन और पेय का भुगतान स्वयं करता है। | युवा लोग बड़ों के लिए पेय परोसते हैं और बड़े भोजन का भुगतान करते हैं। | जो व्यक्ति बड़ा होता है वह हमेशा भुगतान करता है, भले ही निमंत्रण किसने दिया हो। | उम्र की परवाह किए बिना, सभी मेहमानों के बीच बिल को समान रूप से विभाजित करना आम बात है। |
+| **Chinese** | 每个人都支付自己的餐费和饮料费。 | 晚辈为长辈倒酒，长辈支付餐费。 | 无论谁邀请的，年长的人总是付钱。 | 无论年龄大小，所有用餐者平均分摊账单是很常见的。 |
+| **Brazilian Portuguese** | Todos pagam sua própria refeição e bebidas. | Os mais jovens servem bebidas para os mais velhos e os mais velhos pagam a refeição. | A pessoa mais velha sempre paga, independentemente de quem fez o convite. | É comum dividir a conta igualmente entre todos os comensais, independentemente da idade. |
+**Correct Answer**: Option B
+### Sample 2: Morocco (Greeting)
+#### Question
+| Language | Question |
+| :--- | :--- |
+| **English** | In Moroccan culture, how do people typically greet people they know on the street? |
+| **Swahili** | Katika utamaduni wa Moroko, watu huwasalimu vipi watu wanaowajua barabarani? |
+| **Hebrew** | בתרבות המרוקאית, כיצד אנשים מברכים בדרך כלל אנשים שהם מכירים ברחוב? |
+| **Chinese** | 在摩洛哥文化中，人们通常如何问候街上的熟人？ |
+#### Options
+| Language | Option A | Option B | Option C | Option D |
+| :--- | :--- | :--- | :--- | :--- |
+| **English** | By saying "peace be upon you" | By saying "hello" | By hugging them | By giving a casual nod. |
+| **Swahili** | Kwa kusema "amani iwe juu yako" | Kwa kusema "habari" | Kwa kuwakumbatia | Kwa kuinama kichwa kawaida. |
+| **Hebrew** | באמירת "שלום עליכם" | באמירת "שלום" | בחיבוק שלהם | בקידה אגבית. |
+| **Chinese** | 说“愿和平降临于你” | 说“你好” | 拥抱他们 | 随意地点头致意 |
+**Correct Answer**: Option A
+## Usage
+You can load this dataset using the Hugging Face `datasets` library:
+```python
+from datasets import load_dataset
+# Load the dataset
+dataset = load_dataset("Lossfunk/Multilingual_CulturalBench")
+# Access the 'train' split
+train_data = dataset['train']
+# Example: Accessing a Hindi question from the first sample
+print(train_data[0]['hindi_question'])
+print(train_data[0]['hindi_option_a'])