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Finding ways to accelerate text input for individuals with profound motorimpairments has been a long-standing area of research. Closing the speed gapfor augmentative and alternative communication (AAC) devices such aseye-tracking keyboards is important for improving the quality of life for suchindividuals. Recent advances in neural networks of natural language pose newopportunities for re-thinking strategies and user interfaces for enhancedtext-entry for AAC users. In this paper, we present SpeakFaster, consisting oflarge language models (LLMs) and a co-designed user interface for text entry ina highly-abbreviated form, allowing saving 57% more motor actions thantraditional predictive keyboards in offline simulation. A pilot study with 19non-AAC participants typing on a mobile device by hand demonstrated gains inmotor savings in line with the offline simulation, while introducing relativelysmall effects on overall typing speed. Lab and field testing on two eye-gazetyping users with amyotrophic lateral sclerosis (ALS) demonstrated text-entryrates 29-60% faster than traditional baselines, due to significant saving ofexpensive keystrokes achieved through phrase and word predictions fromcontext-aware LLMs. These findings provide a strong foundation for furtherexploration of substantially-accelerated text communication for motor-impairedusers and demonstrate a direction for applying LLMs to text-based userinterfaces.

Using Large Language Models to Accelerate Communication for Users with Severe Motor Impairments