{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["15(1)"],"submitter":["Cai S"],"pubmed_abstract":["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."],"journal":["Nature communications"],"pagination":["9449"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11530652"],"repository":["biostudies-literature"],"pubmed_title":["Using large language models to accelerate communication for eye gaze typing users with ALS."],"pmcid":["PMC11530652"],"pubmed_authors":["Casey B","Cai S","Brenner MP","Kane S","Venugopalan S","Kornman E","Xiao X","Tomanek K","Narayanan A","Vance D","Seaver K","MacDonald RL","Gleason SM","Nelson PQ","Jalasutram S","Morris MR"],"additional_accession":[]},"is_claimable":false,"name":"Using large language models to accelerate communication for eye gaze typing users with ALS.","description":"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.","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Nov","modification":"2025-04-26T17:01:53.886Z","creation":"2025-04-06T15:24:31.674Z"},"accession":"S-EPMC11530652","cross_references":{"pubmed":["39487163"],"doi":["10.1038/s41467-024-53873-3"]}}