biostudies-literatureUnknown15(1)Cai SAccelerating text input in augmentative and alternative communication (AAC) is a long-standing area of research with bearings on the quality of life in individuals with profound motor impairments. Recent advances in large language models (LLMs) pose opportunities for re-thinking strategies for enhanced text entry in AAC. In this paper, we present SpeakFaster, consisting of an LLM-powered user interface for text entry in a highly-abbreviated form, saving 57% more motor actions than traditional predictive keyboards in offline simulation. A pilot study on a mobile device with 19 non-AAC participants demonstrated motor savings in line with simulation and relatively small changes in typing speed. Lab and field testing on two eye-gaze AAC users with amyotrophic lateral sclerosis demonstrated text-entry rates 29-60% above baselines, due to significant saving of expensive keystrokes based on LLM predictions. These findings form a foundation for further exploration of LLM-assisted text entry in AAC and other user interfaces.Nature communications9449https://www.ebi.ac.uk/biostudies/studies/S-EPMC11530652biostudies-literatureUsing large language models to accelerate communication for eye gaze typing users with ALS.PMC11530652Casey BCai SBrenner MPKane SVenugopalan SKornman EXiao XTomanek KNarayanan AVance DSeaver KMacDonald RLGleason SMNelson PQJalasutram SMorris MRfalseUsing large language models to accelerate communication for eye gaze typing users with ALS.Accelerating text input in augmentative and alternative communication (AAC) is a long-standing area of research with bearings on the quality of life in individuals with profound motor impairments. Recent advances in large language models (LLMs) pose opportunities for re-thinking strategies for enhanced text entry in AAC. In this paper, we present SpeakFaster, consisting of an LLM-powered user interface for text entry in a highly-abbreviated form, saving 57% more motor actions than traditional predictive keyboards in offline simulation. A pilot study on a mobile device with 19 non-AAC participants demonstrated motor savings in line with simulation and relatively small changes in typing speed. Lab and field testing on two eye-gaze AAC users with amyotrophic lateral sclerosis demonstrated text-entry rates 29-60% above baselines, due to significant saving of expensive keystrokes based on LLM predictions. These findings form a foundation for further exploration of LLM-assisted text entry in AAC and other user interfaces.2024-01-01T00:00:00Z2024 Nov2025-04-26T17:01:53.886Z2025-04-06T15:24:31.674ZS-EPMC115306523948716310.1038/s41467-024-53873-3