Hidden State

Hidden states, internal representations within machine learning models like large language models (LLMs) and recurrent neural networks (RNNs), are a focus of intense research aimed at understanding model behavior and improving performance. Current work investigates how hidden states encode semantic information, facilitate complex computations (e.g., arithmetic), and reveal model vulnerabilities (e.g., to jailbreak attacks). Analyzing hidden states offers insights into model interpretability, enabling the development of more robust and efficient models, as well as improved methods for data analysis and task-specific adaptation. This research has implications for enhancing model security, improving the accuracy of predictions, and developing more explainable AI systems.