task_categories:conversational size_categories:n<1K language:ar language:zh language:en language:fr language:hi language:es license:apache-2.0 arxiv:2304.07327 region:us
---
license: apache-2.0
task_categories:
- conversational
language:
- ar
- zh
- en
- fr
- hi
- es
size_categories:
- n<1K
dataset_info:
  features:
  - name: prompt
    dtype: string
  splits:
  - name: ar
    num_bytes: 2783
    num_examples: 22
  - name: en
    num_bytes: 2239
    num_examples: 22
  - name: es
    num_bytes: 2361
    num_examples: 22
  - name: fr
    num_bytes: 2685
    num_examples: 22
  - name: hi
    num_bytes: 5373
    num_examples: 24
  - name: zh
    num_bytes: 2111
    num_examples: 24
  download_size: 21140
  dataset_size: 17552
---
# Dataset Card for xOA22 - Multilingual Prompts from OpenAssistant
### Dataset Summary
xOA22 consists of 22 prompts originally shown in Appendix E, page 25 of the [OpenAssistant Conversations paper](https://arxiv.org/pdf/2304.07327.pdf). These 22 prompts were then manually translated by volunteers into 5 languages: Arabic, Simplified Chinese, French, Hindi and Spanish. 

These prompts were originally created for human evaluations of the multilingual abilities of [BLOOMChat](https://huggingface.co/sambanovasystems/BLOOMChat-176B-v1). Since not all prompts could be directly translatable due to cultural and linguistic differences, volunteers were encouraged to make appropriate substitutions and modifications that would maintain the intent of the original English prompt. As this was largely a collaborative, volunteer-led effort, this led to some discrepancies in the number of prompts per language. We make note of major departures from the original English prompt below.
### Languages
- Arabic (ar)
- English (en)
- Spanish (es)
- French (fr)
- Hindi (hi)
- Chinese (zh)

## Dataset Structure
### Data Fields
- `prompt`: manually translated prompt text. The English split is un-modified from the OpenAssistant Converstaions paper.

### Data Splits
The xOA22 dataset has 6 splits, one for each language. Below are the statistics for each split

| Dataset Split | Number of Instances in Split |
| ------------- | ---------------------------- |
| ar            | 22                           |
| en            | 22                           |
| es            | 22                           |
| fr            | 22                           |
| hi            | 24                           |
| zh            | 24                           |

### Translation Notes
Below are notes from volunteer translators. Note that the Hindi split does not include Prompt 4 in the English split.
- Arabic
  - Prompt 12: Second part of the sentence was translated to “Please mainly limit the games to ones that can be played on a PC at home”, discarding the mentions of emulation.
  - Prompt 19: Not sure how to translate "navigation system" to Arabic, I used Google Translate for this one.
- Spanish
  - Prompt 12: IDK how to say crafting system in Spanish. I've always said crafteo.
  - Prompt 21: Not sure how to translate niche, went for "developing a topic" instead
  - Prompt 22: Hacking - hackeo? It's what I'd say colloquially in Spanish, but not sure if it's the right thing to use here
- French
  - No translation notes
- Hindi
  - Prompt 1: Replace "GLaDOS" with "Ravan", a famous antagonist from mythology
  - Prompt 4: This prompt was left untranslated, and so **is missing from the Hindi split**. Translator reasons are as follows: generally people won't ask this in Hindi. Code writing community is generally English aware and they are most probably going to ask this question in English.
  - Prompt 9: Corresponds to English Prompt 10. Specific names changed to well known persons in Hindi speaking world.
  - Prompt 11: Corresponds to English Prompt 12. I removed "in depth crafting system", "directly or through emulation"
  - Prompt 21: Corresponds to English Prompt 22. I removed "social security numbers", "Google", and "Apple"
  - Prompt 22: This is a Hindi-specific prompt. The English translation is: "write me a poem on monsoon in 100 words"
  - Prompt 23: This is a Hindi-specific prompt. The English translation is: "write me a recipe for butter chicken"
  - Prompt 24: This is a Hindi-specific prompt. The English translation is: "How do I go from Delhi to Jaipur? Bus or car? Details please."
- Chinese
  - Prompt 1: Changed GLaDOS to a fictional species from the Chinese sci-fi series The Three Body Problem
  - Prompt 3: Didn't specify whether it's an imaginary world or real world to make it more natural in Chinese. The meaning is basically the same.
  - Prompt 5: Animal rennet -> 人工奶酪 as the former is not familiar to most people
  - Prompt 9: Translated "king" as "emperor" to align with Chinese history
  - Prompt 10: Joe Biden & Joe Rogan -> 毛晓彤 & 光晓彤
  - Prompt 11: Shakespeare -> 鲁迅
  - Prompt 12: "sci-fi ships" -> starship (巨型星际飞船)
  - Prompt 21: YouTube -> b站
  - Prompt 22: social security number -> 身份证
  - Prompt 23: This is a Chinese-specific prompt. The English translation is: "Explain Kubernetes in simple terms. Explain to me like I'm 11 years old."
  - Prompt 24: This is a Chinese-specific prompt. The English translation is: "I will provide you with an argument or opinion of mine. I want you to criticize it as if you were Elon Musk".
    - Translator note: I don't think there is a good counterpart entrepreneur like Elon Musk in China. Jack Ma is as wealthy and powerful as Elon Musk but they have quite different perspectives. So instead of finding an actual counterpart in China, we need to understand the characteristics of Elon Musk and translate accordingly. 

### Curation Rationale
These prompts were originally curated in order to test the multilingual abilities of the BLOOMChat model. The model's responses to these translated prompts were compared to responses from other open-source chat models in a human evaluation study. Therefore, emphasis was placed on making translations as natural and understandable as possible to native speakers in order to emulate a chat setting, and we accepted feedback and modifications to the prompts from our volunteers.

### Dataset Curators

### Contributions

### Source Data
Appendix E, page 25 of ["OpenAssistant Conversations - Democratizing Large Language Model Alignment"](https://arxiv.org/pdf/2304.07327.pdf)