task_categories:conversational task_categories:question-answering task_categories:text-generation size_categories:1K<n<10K language:en license:apache-2.0 Physics Biology Math Chemistry Culture Logic Roleplay region:us
---
license: apache-2.0
task_categories:
- conversational
- question-answering
- text-generation
language:
- en
tags:
- Physics
- Biology
- Math
- Chemistry
- Culture
- Logic
- Roleplay
pretty_name: Pure-Dove
size_categories:
- 1K<n<10K
---

## This is the Official Pure-Dove dataset. Over 3K multi-turn examples, and many more coming soon!

This dataset aims to be the largest highest quality cluster of real human back and forth conversations with GPT-4.

Steps have even been done to ensure that only the best GPT-4 conversations in comparisons are kept, there are many instances where two GPT-4 responses are rated as equal to eachother or as both bad. We exclude all such responses from Pure Dove and make sure to only include ChatBot Arena responses that are voted as being better even against another instance of GPT-4.

 - Comprised of over 3000 highly filtered multi-turn conversations between GPT-4 and real humans.

 - Average context length per conversation is over 800 tokens. 

## Purpose?

 - This dataset is not particularly intended to be trained on by itself, however, the size and quality of this dataset can work wonderfully as a supplemmentary addition to virtually any multi-turn compatible dataset. I encourage this use, all I ask is proper credits given for such! 

## Quality filtering and cleaning.

 - The conversations were sourced from openly datasets such as ShareGPT and ChatBotArena by Lmsys, however, a large portion of these chats were riddled with hallucinations and abnormal distributions of different languages.   

 - Extensive cleaning was done to filter out instances of overt AI moralizing or related behaviour, such as "As an AI language model" and "September 2021", not just in english, but other languages too!

## Credits

During the curation process, there can be some relatively arduos steps when it comes to actually executing on the best experimentation or concepts for how to filter examples out.

Luckily there is folks over at NousResearch that helped expedite this process with little to no sacrifices in quality, big credit to J-Supha within NousResearch specifically for making these types of significant contributions.


## Future Plans & How you can help!

This is a relatively early build amongst the grand plans for the future of what I plan to work on! 

In the near future we plan on leveraging the help of domain specific expert volunteers to eliminate any mathematically/verifiably incorrect answers from training curations of different types of datasets.

If you have at-least a bachelors in mathematics, physics, biology or chemistry and would like to volunteer even just 30 minutes of your expertise time, please contact LDJ on discord!