Hidden Representation

Hidden representations, the internal data structures generated by neural networks at intermediate layers, are a key focus in understanding and improving AI models. Current research investigates how these representations encode information, focusing on their geometric properties, their role in various tasks (like language modeling and image processing), and their manipulation for controlling model behavior (e.g., through activation steering or contrastive instruction tuning). Understanding hidden representations is crucial for enhancing model interpretability, improving robustness to adversarial attacks and out-of-distribution data, and developing more efficient and reliable AI systems across diverse applications.