Self Supervised Probing

Self-supervised probing is a technique used to analyze the internal representations of machine learning models, particularly deep neural networks, without relying on labeled data. Current research focuses on improving the reliability and interpretability of these probes, addressing issues like bias in datasets and the distinction between genuine and spurious knowledge encoded within the model. This approach is crucial for enhancing the trustworthiness of AI systems by identifying and mitigating overconfidence, improving model calibration, and ultimately leading to more robust and reliable AI applications across various domains. Furthermore, it aids in understanding the internal workings of complex models, facilitating the development of more effective and explainable AI.