Generating MCQs from Wikipedia Articles Using RAG

In this tutorial, we will explore how to use Retrieval-Augmented Generation (RAG) to generate multiple-choice questions (MCQs) from Wikipedia articles. This process allows you to create tailored questions based on specific contexts defined by the user. Whether you are an educator looking to create quizzes or a learner wanting to test your knowledge, this guide will help you understand the key steps involved.

Prerequisites

Before we dive into the steps, make sure you have the following:

• A basic understanding of Python programming.
• Familiarity with machine learning concepts.
• Access to a Python environment with necessary libraries installed, such as Hugging Face Transformers.
• A Wikipedia article or topic in mind that you want to generate MCQs from.

Step-by-Step Guide

Step 1: Setting Up Your Environment

First, ensure that you have Python installed on your machine. You can download it from python.org. Next, install the required libraries using pip:

pip install transformers datasets

Step 2: Loading the RAG Model

Once your environment is set up, you can load the RAG model. RAG combines retrieval and generation capabilities, making it ideal for our task. Here’s how to load the model:

from transformers import RagTokenizer, RagRetriever, RagSequenceForGeneration

tokenizer = RagTokenizer.from_pretrained('facebook/rag-sequence-base')
retriever = RagRetriever.from_pretrained('facebook/rag-sequence-base')
model = RagSequenceForGeneration.from_pretrained('facebook/rag-sequence-base')

Step 3: Defining Your Context

Now, you need to define the context from which you want to generate MCQs. This could be a specific section of a Wikipedia article or a general topic. For example:

context = "The solar system consists of the Sun and the objects that orbit it, including eight planets, their moons, and other celestial bodies."

Step 4: Generating Questions

With the context defined, you can now generate questions. Use the model to create MCQs based on the provided context:

input_ids = tokenizer([context], return_tensors='pt').input_ids
outputs = model.generate(input_ids)
questions = tokenizer.batch_decode(outputs, skip_special_tokens=True)

Step 5: Formatting the MCQs

After generating the questions, you may want to format them into a multiple-choice format. Here’s a simple way to do that:

for question in questions:
    print(f"Question: {question}")
    print("Options: A, B, C, D")

Explanation of Key Concepts

Let’s break down some of the key concepts involved in this process:

• Retrieval-Augmented Generation (RAG): RAG is a model that combines retrieval of relevant documents with the generation of text. It allows for more contextually relevant outputs.
• Context: In this tutorial, context refers to the specific information or topic from which you want to generate questions.
• MCQs: Multiple-choice questions are a common format for assessments, where a question is posed, and several answer options are provided.

Conclusion

In this tutorial, we covered the essential steps to use RAG for generating multiple-choice questions from Wikipedia articles based on user-defined context. By following these steps, you can create customized quizzes that enhance learning and engagement. Experiment with different contexts and see how the generated questions vary!

For further reading, check out the original post How to Build an MCQ App”>here. This tutorial was inspired by content from Towards Data Science”>this source.