Perturbation: A simple and efficient adversarial tracer for representation learning in language models
The episode discusses "Perturbation," an efficient adversarial tracer for language models. It shows how this tool makes model representations measurable, moving AI interpretability beyond guesswork. This allows researchers to stress-test models for robustness and bias, thereby increasing transparency and trust in advanced AI systems.