# EasyQA: A Kindergarten-Level QA Dataset for Investigating Truthfulness.

EasyQA is a GPT-3.5-turbo-generated dataset of easy kindergarten-level facts, meant to be used to prompt and evaluate large language models for "common-sense" truthful responses. This dataset was originally created to understand how different types of truthfulness may be represented in the intermediate activations of large language models. EasyQA compromises 2346 questions that span 50 categories, including art, technology, education, music, and animals. The questions are meant to be extremely simple and obvious, eliciting an obvious truth that would not be susceptible to misconceptions -- making it an excellent comparison compared to benchmarks related to other types of truth (e.g. TruthfulQA, which focuses on common misconceptions).

Credits to Kevin Wang, Richard Ren, and Phillip Guo.

## Dataset Creation

The dataset was created by prompting GPT-3.5-turbo with: "*Please generate 50 easy, obvious, common-knowledge questions that a kindergartener would learn in class about the topic prompted, as well as correct and incorrect responses. These questions should be less like trivia questions (i.e. Who is known as the Queen of Jazz?) and more like obvious facts (ie What color is the sky?). Your generations should be in the format: Question: {Your question here} Right: {Right answer} Wrong: {Wrong answer} where each question is a new line. Please follow this format verbatim (e.g. do not number the questions).*"

The following categories were used:
```
Animals
Plants
Food and drink
Music
Movies
Television shows
Literature
Sports
Geography
History
Science
Mathematics
Art
Technology
Politics
Business and Economy
Education
Health and Fitness
Environment and Climate
Space and Astronomy
Fashion and Style
Video Games
Travel and Tourism
Language and Literature
Religion and Spirituality
Famous Personalities
Cultural Events/Festivals
Cars and Automobiles
Photography
Architecture
Medicine and Health
Psychology
Philosophy
Law
Social Sciences
Human Rights
Current Events/News
Global Affairs
National Landmarks
Celebrities and Entertainment
Nature
Cooking and Baking
Gardening
DIY Projects
Dance
Comic Books and Graphic Novels
Mythology and Folklore
Internet and Social Media
Parenting and Family Life
Home Decor
```
task_categories:question-answering language:en license:apache-2.0 region:us
{"language": ["en"], "license": "apache-2.0", "task_categories": ["question-answering"], "pretty_name": "Easy Question Answer"}
TAGS
#task_categories-question-answering #language-English #license-apache-2.0 #region-us 

# EasyQA: A Kindergarten-Level QA Dataset for Investigating Truthfulness.

EasyQA is a GPT-3.5-turbo-generated dataset of easy kindergarten-level facts, meant to be used to prompt and evaluate large language models for "common-sense" truthful responses. This dataset was originally created to understand how different types of truthfulness may be represented in the intermediate activations of large language models. EasyQA compromises 2346 questions that span 50 categories, including art, technology, education, music, and animals. The questions are meant to be extremely simple and obvious, eliciting an obvious truth that would not be susceptible to misconceptions -- making it an excellent comparison compared to benchmarks related to other types of truth (e.g. TruthfulQA, which focuses on common misconceptions).

Credits to Kevin Wang, Richard Ren, and Phillip Guo.

## Dataset Creation

The dataset was created by prompting GPT-3.5-turbo with: "*Please generate 50 easy, obvious, common-knowledge questions that a kindergartener would learn in class about the topic prompted, as well as correct and incorrect responses. These questions should be less like trivia questions (i.e. Who is known as the Queen of Jazz?) and more like obvious facts (ie What color is the sky?). Your generations should be in the format: Question: {Your question here} Right: {Right answer} Wrong: {Wrong answer} where each question is a new line. Please follow this format verbatim (e.g. do not number the questions).*"

The following categories were used:

# EasyQA: A Kindergarten-Level QA Dataset for Investigating Truthfulness.

EasyQA is a GPT-3.5-turbo-generated dataset of easy kindergarten-level facts, meant to be used to prompt and evaluate large language models for "common-sense" truthful responses. This dataset was originally created to understand how different types of truthfulness may be represented in the intermediate activations of large language models. EasyQA compromises 2346 questions that span 50 categories, including art, technology, education, music, and animals. The questions are meant to be extremely simple and obvious, eliciting an obvious truth that would not be susceptible to misconceptions -- making it an excellent comparison compared to benchmarks related to other types of truth (e.g. TruthfulQA, which focuses on common misconceptions).

Credits to Kevin Wang, Richard Ren, and Phillip Guo. ## Dataset Creation

The dataset was created by prompting GPT-3.5-turbo with: "*Please generate 50 easy, obvious, common-knowledge questions that a kindergartener would learn in class about the topic prompted, as well as correct and incorrect responses. These questions should be less like trivia questions (i.e. Who is known as the Queen of Jazz?) and more like obvious facts (ie What color is the sky?). Your generations should be in the format: Question: {Your question here} Right: {Right answer} Wrong: {Wrong answer} where each question is a new line. Please follow this format verbatim (e.g. do not number the questions).*"

The following categories were used:
TAGS
#task_categories-question-answering #language-English #license-apache-2.0 #region-us 
 # EasyQA: A Kindergarten-Level QA Dataset for Investigating Truthfulness.

EasyQA is a GPT-3.5-turbo-generated dataset of easy kindergarten-level facts, meant to be used to prompt and evaluate large language models for "common-sense" truthful responses. This dataset was originally created to understand how different types of truthfulness may be represented in the intermediate activations of large language models. EasyQA compromises 2346 questions that span 50 categories, including art, technology, education, music, and animals. The questions are meant to be extremely simple and obvious, eliciting an obvious truth that would not be susceptible to misconceptions -- making it an excellent comparison compared to benchmarks related to other types of truth (e.g. TruthfulQA, which focuses on common misconceptions).

Credits to Kevin Wang, Richard Ren, and Phillip Guo. ## Dataset Creation

The dataset was created by prompting GPT-3.5-turbo with: "*Please generate 50 easy, obvious, common-knowledge questions that a kindergartener would learn in class about the topic prompted, as well as correct and incorrect responses. These questions should be less like trivia questions (i.e. Who is known as the Queen of Jazz?) and more like obvious facts (ie What color is the sky?). Your generations should be in the format: Question: {Your question here} Right: {Right answer} Wrong: {Wrong answer} where each question is a new line. Please follow this format verbatim (e.g. do not number the questions).*"

The following categories were used:
passage: TAGS
#task_categories-question-answering #language-English #license-apache-2.0 #region-us 
# EasyQA: A Kindergarten-Level QA Dataset for Investigating Truthfulness.

EasyQA is a GPT-3.5-turbo-generated dataset of easy kindergarten-level facts, meant to be used to prompt and evaluate large language models for "common-sense" truthful responses. This dataset was originally created to understand how different types of truthfulness may be represented in the intermediate activations of large language models. EasyQA compromises 2346 questions that span 50 categories, including art, technology, education, music, and animals. The questions are meant to be extremely simple and obvious, eliciting an obvious truth that would not be susceptible to misconceptions -- making it an excellent comparison compared to benchmarks related to other types of truth (e.g. TruthfulQA, which focuses on common misconceptions).

Credits to Kevin Wang, Richard Ren, and Phillip Guo.## Dataset Creation

The dataset was created by prompting GPT-3.5-turbo with: "*Please generate 50 easy, obvious, common-knowledge questions that a kindergartener would learn in class about the topic prompted, as well as correct and incorrect responses. These questions should be less like trivia questions (i.e. Who is known as the Queen of Jazz?) and more like obvious facts (ie What color is the sky?). Your generations should be in the format: Question: {Your question here} Right: {Right answer} Wrong: {Wrong answer} where each question is a new line. Please follow this format verbatim (e.g. do not number the questions).*"

The following categories were used: