LLM-Generated-Text-Detection-Using-Machine-Learning

Overview

This project implements a Logistic Regression based machine learning pipeline to detect whether a piece of text is human-generated or AI-generated by large language models (LLMs). It uses TF-IDF vectorization and standard machine learning preprocessing steps, training, evaluation, and inference. The goal is to provide a reliable and interpretable method to distinguish AI-generated text.

Dataset

The datasets used are publicly available from Kaggle:

• LLM-Detect AI Generated Text (DAIGT)
  https://www.kaggle.com/datasets/sunilthite/llm-detect-ai-generated-text-dataset/data

• Augmented Dataset for LLM-Detect
  https://www.kaggle.com/datasets/sunilthite/augmented-data-for-llm-detect-ai-generated-text

Please download these datasets and place them in the /data folder or update the data path in the notebook before running.

Features

• Text preprocessing including cleaning and balancing of classes
• TF-IDF vectorization using unigrams and bigrams
• Logistic Regression model training and evaluation
• Performance metrics: Accuracy, Precision, Recall, F1-score, and Confusion Matrix visualization
• Model serialization using pickle for saving and loading
• Sample code for inference on custom input text

Installation

1. Clone the repository:

   git clone https://github.com/areebaghazal88/LLM-Generated-Text-Detection-Using-Machine-Learning.git
   cd LLM-Generated-Text-Detection-Using-Machine-Learning

2. (Optional) Create and activate a Python virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:

   pip install -r requirements.txt

Usage

1. Prepare the Dataset Download and place the Kaggle datasets in the /data directory.

2. Run the notebook or script Open LLM_Text_Detection.ipynb in your preferred Jupyter environment (Google Colab, Jupyter Notebook, VSCode, etc.) to:

   • Load and preprocess the dataset
   • Train the Logistic Regression model
   • Evaluate the model and visualize metrics

3. Make Predictions Example for inference on new text:

   import pickle
   
   # Load model and vectorizer
   with open('logistic_regression_model.pickle', 'rb') as f:
       logreg = pickle.load(f)
   with open('tfidf_vectorizer.pickle', 'rb') as f:
       tfidf_vectorizer = pickle.load(f)
   
   # Sample text prediction
   text = "Once upon a time in a forest, a little girl met three bears..."
   vectorized = tfidf_vectorizer.transform([text])
   prediction = logreg.predict(vectorized)
   label = "human-generated" if prediction == 0 else "AI-generated"
   print("Prediction:", label)

Dependencies

• Python 3.x
• scikit-learn
• pandas
• numpy
• seaborn
• matplotlib
• joblib

Install them via:

pip install -r requirements.txt

GUI Interface

A screenshot of the graphical user interface (GUI) is included in this repository as image.png.
The GUI provides a user-friendly way to interact with the AI-generated text detection system, allowing users to input text and receive real-time detection results.

License

This project is licensed under the MIT License — see the LICENSE file for details.

Thank you for checking out this project! Contributions and suggestions are welcome.