Text Entry

Text entry research aims to improve the speed, accuracy, and ease of typing on digital devices, focusing on methods that reduce cognitive load and enhance user experience. Current efforts involve developing advanced autocomplete systems using reinforcement learning to optimize suggestion policies based on user interaction and incorporating large language models for intelligent text prediction, often through prompt engineering techniques. These advancements leverage models like ByT5 and GPT-3.5, and the creation of large datasets like FSboard for sign language recognition is crucial for training and evaluating these systems. Ultimately, these improvements have significant implications for accessibility (e.g., for sign language users) and overall user productivity across various digital platforms.