RANDOMNESSPAPER

This repository contains the supplementary materials for the paper titled "Integrating Randomness in Large Language Models: A Linear Congruential Generator Approach for Generating Clinically Relevant Content".

Table of Contents

• Overview
• Structure
• Installation
• Usage
• License
• Contact

Overview

The generation of diverse, high-quality outputs from language models (LLMs) is essential for various applications, including education and content creation. Achieving true randomness and avoiding repetition remains a significant challenge. This study employs the Linear Congruential Generator (LCG) method for systematic fact selection, combined with AI-powered content generation. Using LCG, we ensured unique combinations of gastrointestinal physiology and pathology facts across multiple rounds, integrating these facts into prompts for GPT-4o to create clinically relevant, vignette-style outputs.

This repository includes all necessary scripts and data files to reproduce the results presented in the paper.

Structure

The repository is organized as follows:

• fact_mapping.py - Script for mapping facts to indices.
• mcq_generation_summary_lcg.csv - CSV file summarizing the MCQ generation rounds using LCG.
• python.py - Main script for generating MCQs using LCG and GPT-4o.
• data/ - Directory to store additional data files.
• env/ - Directory for the virtual environment setup (if used).
• LICENSE - License file for the repository.
• README.md - This file.
• requirements.txt - List of Python dependencies required for the project.

Installation

To set up the environment and install the required dependencies, follow these steps:

1. Clone the repository:

   git clone https://github.com/andrewbouras/RANDOMNESSPAPER.git
   cd RANDOMNESSPAPER

2. Create and activate a virtual environment (optional but recommended):

   python3 -m venv env
   source env/bin/activate

3. Install the required packages:

   pip install -r requirements.txt

Usage

To generate MCQs using the LCG method and GPT-4, follow these steps:

1. Ensure the base prompt and fact mapping are correctly set up in the Concept Prompts/ directory.

2. Run the python.py script to start the generation process:

   python Concept\ Prompts/python.py

3. The generated MCQs and their summaries will be saved in the mcq_generation_summary_lcg.csv file.

License

This project is licensed under the MIT License.

Contact

For any questions or inquiries, please contact:

Andrew Bouras, B.S.
Nova Southeastern University, College of Osteopathic Medicine
Email: ab4646@mynsu.nova.edu