Efficient GRE Question Generation with ScholarDen

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Automating Scholar Den's Test Question Creation

Scholar Den is a platform devoted to helping students excel on the GRE by offering the tools and resources they need for success. As the platform grew in popularity, it became clear that creating new test questions quickly and consistently was becoming a challenge. The traditional approach required a lot of time and effort from the team, making it hard to keep up with demand and still maintain the quality of the content students relied on.

The Challenge

Scholar Den’s biggest hurdle was the slow and manual process of generating test questions. Dedicated employees spent significant time crafting each question, only for it to go through another round of review to ensure quality. This method was not only time-consuming but also put a strain on the team’s productivity, slowing down the creation of fresh, high-quality test content.

How Did We Take on the Challenge?

Veritas Analytica addressed Scholar Den’s challenges by conducting a thorough analysis of their existing question-generation process. We developed a comprehensive strategy that included curating a diverse and high-quality dataset, implementing customizable prompt inputs, and refining the model’s output through continuous fine-tuning. Additionally, we created a streamlined application that integrated all these components seamlessly, enhancing usability and efficiency.

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The Roadblocks

  • Acquiring a Comprehensive Dataset: Needed a diverse and high-quality dataset suitable for training the Language Model (LLM).
  • Implementing Customizable Prompt Inputs: Designed an effective mechanism for users to input customizable prompts.
  • Refining Output Through Fine-Tuning: Achieved optimal performance through continuous refinement and adjustment of various parameters.
  • Developing a Streamlined Application: Integrated multiple components into a single, user-friendly application.

Features of Our Solution

Automated Processes

Automated Processes

Enhanced efficiency and productivity with a user interface application.

Customizable Dataset

Customizable Dataset

Offered runtime dataset modification, segregated according to the difficulty level.

Default Prompt Modification

Default Prompt Modification

Featured a robust prompt modification button, allowing users to set prompts according to their needs.

Rapid Response

Rapid Response

Generated up to 50 questions with a response time of less than a minute.

Tech Stack

Tech Stack Used

  • Meta Llama 3 : LLaMA 3 is a large language model (LLM) developed by Meta, Integrated via API to generate questions, enhancing the application’s natural language processing and user interactions.
  • Python : Utilizing Python, we developed an application and graphical user interface (GUI) with Tkinter, which simplifies building interactive applications with minimal code.

Solution

Curated Dataset Acquisition

Curated Dataset Acquisition

Gathered a comprehensive dataset of questions tailored for the model’s training needs.

Interactive Prompt Customization

Interactive Prompt Customization

Provided intuitive customization options through an interactive application interface.

Output Optimization

Output Optimization

Refined the model’s outputs to ensure high-quality results.

Unified Application Integration

Integrated all processes into a single application, enabling users to perform every task efficiently with a single click.

Impact

  • Accuracy: Generated questions matched the set difficulty level, with a user acceptance rate of up to 60%.
  • Enhanced User Experience: Improved overall efficiency and productivity with the new application-based system.
  • Real-Time Modification: Enabled real-time modification of prompts and datasets for quicker adjustments.
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Data Flow Diagram

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Client Testimonial

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Date: 07/24/2024

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