AITutor-Backend is a part of the LMaAiT-BE (Large Language Models as Actors in Text-Based Environments) project, focusing on leveraging Large Language Models for creating interactive, text-based educational environments. This is an ongoing research project for USF IEEE AI Group. Our group is commited to developing research projects relevant to modern approaches and technologies.
Note: This application is designed to run with USF-IEEE/AITutor_Frontend. Find that repo here.
- Jonathan Koch jkoch21@usf.edu, University of South Florida
- Fiorella Ratti Tamayo frattitamayo@usf.edu, University of South Florida
- Juan Gomez juangomez@usf.edu, University of South Florida
To set up the AITutor-Backend environment, follow these steps:
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Clone the repository:
git clone https://github.com/USF-IEEE/AITutor-Backend cd AITutor-Backend -
Set up a virtual environment (using conda or venv):
# Example using venv python -m venv env source env/bin/activate # On Windows use `env\Scripts\activate` -
Install the required dependencies:
pip install -r requirements.txt -
Create and migrate the database:
python manage.py makemigrations AITutor_Backend ./run_migrations.sh # On Windows use `run_migrations.bat`
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To run the unit tests (GENERATION_TESTS enabled only if specified):
GENERATION_TESTS=1 python test.py -
To start the backend server:
DEBUG=1 python run.py -
To run the console demo:
python prompt_runner.py
Abstract: The LMaAiT-BE project explores the capabilities of Large Language Models in an interactive text-based educational environment. This research investigates how these models can be utilized for automated education, leveraging their problem-solving and generative abilities.
Introduction: The project builds on the advancements in Transformer architecture and the emergent properties of LLMs in various domains such as creative writing, source citation, and chatbot applications.
- Actor Generation Content: Central focus concepts, knowledge graphs, slides/presentations, and examination questions.
- Metrics: Evaluation includes knowledge graph accuracy, slide/presentation relevance, and question generation quality, measured via cosine similarity of vector embeddings.
The project delves into cognitive abilities like planning, translation, discrimination, and more, showcasing the potential of LLMs in simulating human-like cognitive processes.
Discusses the underlying hypotheses and theoretical foundations, including discussions on cognitive processes, algorithmic pattern matching, and the role of language in intelligence.
Access the full research paper here.