Reproduce “A Multimodal Automated Interpretability Agent” on OpenResearch
Turn your favorite coding agents into research agents that review literature, develop hypotheses, and run experiments.
Universal app for Apple silicon and Intel. Requires macOS 11 or later.
About this paper
Tamar Rott Shaham, Sarah Schwettmann, Franklin Wang, Achyuta Rajaram, Evan Hernandez, Jacob Andreas, Antonio Torralba
MAIA (Multimodal Automated Interpretability Agent) automates neural network understanding by leveraging a vision-language model (VLM) as an agent to iteratively experiment with and describe neural network components. This system generates predictive explanations for neuron behavior, matches human expert performance in description tasks, and successfully aids in identifying spurious features and model biases, improving model robustness.
Key insights
- Combining powerful VLM reasoning with a dedicated, iterative experimental toolkit can effectively automate complex, human-like interpretability tasks.
- High-quality descriptions of neuron behavior are more predictive when derived from an iterative process involving both real-world examples and causally controlled synthetic data.
- Automated interpretability agents can provide actionable insights for critical downstream tasks like mitigating spurious correlations and identifying model biases, thereby improving model robustness and fairness.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.