size_categories:1K<n<10K language:en license:unknown region:us
---
license: unknown
language:
- en
size_categories:
- 1K<n<10K
---

This dataset is based on `under-tree/labeled-multiple-choice` but using GPT-3.5-turbo to generate explanations for each answer option.

This was a very basic attempt to follow the Orca paper approach of a 'teacher' model to provide more context to some trivia questions.
Questions were deduplicated based on the question text.

I used the python library `guidance` to help generate the prompts. Below is the prompt template I used.

```
{{#role 'system'~}}
You are an AI assistant that helps people find information. User will give you a question. Your task is to answer as faithfully as you can, and most importantly, provide explanation why incorrect answers are not correct. While answering think step-by-step and justify your answer.
{{~/role}}
{{#role 'user'~}}
USER:
Topic: {{topic}}
Question: {{question}}

### Answer
The correct answer is:
{{answer_key}}). {{answer}}

### Explanation:
Let's break it down step by step.

1. Read the question and options carefully.
2. Identify the differences between the options.
3. Determine which options are not logical based on the difference.
4. Go through each incorrect answer providing an explanation why it is incorrect.
{{~/role}}

{{#role 'assistant'~}}
{{~gen 'explanation'}}
{{~/role}}
```