region:us
# Augmentatio-Orci

## Overview

This dataset card describes the augmentation process aimed at improving the quality and depth of responses. The main motivation behind this augmentation was to overcome the limitations of system-generated prompts and to generate more detailed and nuanced outputs.

## Custom Prompting Process

### Introduction

The custom prompting process was formulated with a focus on analytical depth and detailed responses. This approach was designed to replace the system-generated prompts that often produced flat and non-detailed outputs.

### Comparison between GPT-4 and text-davinci-003

The augmentation method involves a comparison between the given example generated by GPT-4 and the custom prompts, as well as a replacement for the system prompt which outlines byside thinking by providing an example for how to think of the output by explaining the step by step reasoning to arrive at the answer in simple and easily understood terms.
Here's a comparison using the provided GPT-4 example:

**GPT-4 Example**:


system: "You are a helpful assistant, who always provide explanation. Think like you are answering to a five year old."


input: "What happens next in this paragraph? She then rubs a needle on a cotton ball then pushing it onto a pencil and wrapping thread around it. She then holds up a box of a product and then pouring several liquids into a bowl. she Choose your answer from: A. adds saucepan and shakes up the product in a grinder. B. pinches the thread to style a cigarette, and then walks away. C. then dips the needle in ink and using the pencil to draw a design on her leg, rubbing it off with a rag in the end. D. begins to style her hair and cuts it several times before parting the ends of it to show the hairstyle she has created."


output: "C. She then dips the needle in ink and using the pencil to draw a design on her leg, rubbing it off with a rag in the end. In this option, she is continuing the process of using the needle, pencil, and thread, which is most related to what she was doing in the previous sentence."


**text-davinci-003 Response**:


"input": "Given the task definition and input, reply with output. In this task, you're given a context, a sentence,  and a character. The sentence describes an action or job of the given character. Also, the context provides more information about the sentence or the character. Your task is to write the character's motivation by doing a specific job, which is given in the sentence. You can use context to find the motivation; however, all the inputs do not provide context. Also, in some inputs, there can be zero motivation; output, 'None' in this case.\n\nContext: None \n Sentence: Dalene taught theory to the cosmetology students at Olympian Academy. \n Character: Olympian academy",


"output": "The task requires us to determine the character's motivation for teaching theory to the cosmetology students at Olympian Academy based on the given sentence and context. However, in this case, there is no context provided.\n\nTo start with, the sentence states that Dalene taught theory to the cosmetology students at Olympian Academy. We need to analyze this sentence and the character, Olympian Academy, to identify their motivation.\n\nFirstly, we know that Dalene is the one teaching theory to the cosmetology students, which implies that she possesses knowledge and expertise in this subject. This suggests that the motivation for Olympian Academy could be to ensure that their students receive a well-rounded education, including theoretical knowledge, to enhance their skills and prepare them for their future careers in cosmetology.\n\nAnalyzing the character, Olympian Academy, we can gather that it is an educational institution specializing in cosmetology. As an educational institution, their primary goal would be to provide high-quality education to their students. By offering theoretical teaching in addition to practical training, they are better equipping their students with a comprehensive understanding of cosmetology, which can potentially lead to higher success rates and employment opportunities for their graduates.\n\nFurthermore, the inclusion of theory in the curriculum also indicates that Olympian Academy aims to develop the critical thinking and analytical skills of their students. By teaching theory, they foster a deeper understanding of the subject matter, enabling the students to apply their knowledge creatively and adapt to different situations within the field of cosmetology.\n\nConsidering all of these points, it can be concluded that the character's motivation, Olympian Academy, for having Dalene teach theory to the cosmetology students is to provide a comprehensive education, improve student success and employment prospects, and cultivate critical thinking and analytical skills. Therefore, the output would be a motivation statement reflecting these motivations.\n\nIn conclusion, based on the analysis of the given sentence and the character, Olympian Academy, the motivation for teaching theory to the cosmetology students is to provide a comprehensive education, enhance student success and employability, and foster critical thinking and analytical skills."



### Flaw Addressed: Lack of Detail

A significant flaw in the original system-generated prompts was identified as a lack of detail. This was evident in certain responses that were inconsistent with the complexity of the questions. The decision to employ text-davinci-003 and compare it with GPT-4 was a targeted approach to demonstrate this flaw.

## Initial Testing Subset

The initial subset for testing comprises approximately 250,000 examples, with 15,000 examples processed through the custom prompting stage from the 'Ones flan's t0' subset.

- **Total Examples**: 250,000 examples from various sets.
- **Processed Examples**: 15,000 examples from 'Ones flan's t0' subset.

The dataset’s augmentation process represents an exploration into enhancing machine-generated responses. By contrasting the responses of two models, GPT-4 and text-davinci-003, the project illuminates the importance of prompt design and model selection in generating detailed and nuanced content.

The initial subset provides a solid foundation for testing, with future updates planned to refine and expand the data. The emphasis on detail and the strategic use of different models mark a significant step towards a more thoughtful and effective approach to machine-generated content. Future work will continue to build on these insights and methodologies.


# todo: 
determine if the step by step logic should be included in the prompt along with the correct answer to generate more detailed variants of the answers
determine if the spread of data is diverse enough to create a discriptive view of the overall element were teaching
determine what needs to be removed from the initial set to avoid wasting as much compute as possible
determine if negative examples should be included for conditional training
determine if the complexity of the task should be scaled over the course of training to improve performance.