LLM SATs FTW

A collection of experiments exploring Large Language Models (LLMs) and their performance on SAT-style tasks, powered by Streamlit.

Features

• Multiple LLM-based experiments
• Interactive Streamlit UI
• Easy to extend and customize

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/talk-llm-sats-ftw-code.git
   cd talk-llm-sats-ftw-code

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

   python3 -m venv venv
   source venv/bin/activate

3. Install dependencies:

   pip install -r requirements.txt

OpenAI API Key Required

To use these experiments, you must provide your own OpenAI API key. You can obtain an API key by signing up at OpenAI. The app will prompt you to enter your key when you run an experiment.

Running Experiments

Each experiment is implemented as a separate Streamlit app in the main directory, named experiment-<number>.py.

To run an experiment, use:

streamlit run *.py

For example:

streamlit run experiment-1-starburst.py

Available Experiments

• experiment-1-starburst.py: Starburst
• experiment-2-ach.py: Analysis of Competing Hypotheses (ACH)
• experiment-3-kac.py: Key Assumptions Check (KAC)

License

MIT License