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

## This is the Official LessWrong-Amplify-Instruct dataset. Over 500 multi-turn examples, and many more coming soon!

 - This leverages Amplify-Instruct method to extend thousands of scraped Less-Wrong posts into advanced in-depth multi-turn conversations.

 - Comprised of over 500 highly filtered multi-turn conversations between GPT-4 and real humans.

 - Average context length per conversation is over 2,000 tokens. (will measure this more accurately soon)

 - Synthetically created using a newly developed pipeline that leverages GPT-4 to dynamically role play and inquire as the human and assistant.

 - Each conversation is optimized to amplify the raw knowledge retreival of the model and delve deep into obscure and advanced topics.

## Purpose?

 - This dataset is not 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.

 - 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"

## 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 thank you to J-Supha 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!