title: Using Large Language Models to Accelerate Communication for Users with
    Severe Motor Impairments
summary: Finding ways to accelerate text input for individuals with profound motor
  impairments has been a long-standing area of research. Closing the speed gap
  for augmentative and alternative communication (AAC) devices such as
  eye-tracking keyboards is important for improving the quality of life for such
  individuals. Recent advances in neural networks of natural language pose new
  opportunities for re-thinking strategies and user interfaces for enhanced
  text-entry for AAC users. In this paper, we present SpeakFaster, consisting of
  large language models (LLMs) and a co-designed user interface for text entry in
  a highly-abbreviated form, allowing saving 57% more motor actions than
  traditional predictive keyboards in offline simulation. A pilot study with 19
  non-AAC participants typing on a mobile device by hand demonstrated gains in
  motor savings in line with the offline simulation, while introducing relatively
  small effects on overall typing speed. Lab and field testing on two eye-gaze
  typing users with amyotrophic lateral sclerosis (ALS) demonstrated text-entry
  rates 29-60% faster than traditional baselines, due to significant saving of
  expensive keystrokes achieved through phrase and word predictions from
  context-aware LLMs. These findings provide a strong foundation for further
  exploration of substantially-accelerated text communication for motor-impaired
  users and demonstrate a direction for applying LLMs to text-based user
  interfaces.
paper_authors._id: 656e98257112260ee14239ba
paper_authors.name: Shanqing Cai
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paper_authors.fullname: Shanqing Cai
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paper_authors.name: Subhashini Venugopalan
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paper_authors.fullname: Subhashini
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paper_authors.user: vsubhashini
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paper_authors.name: Katie Seaver
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paper_authors.name: Xiang Xiao
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paper_authors.name: Katrin Tomanek
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paper_authors.name: Sri Jalasutram
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paper_authors.name: Meredith Ringel Morris
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paper_authors.name: Shaun Kane
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paper_authors.fullname: Shaun Kane
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paper_authors.user: shaunkane
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paper_authors.name: Ajit Narayanan
paper_authors._id: 656e98257112260ee14239c3
paper_authors.name: Robert L. MacDonald
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paper_authors.fullname: Robert MacDonald
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paper_authors.user: icemunk
paper_authors._id: 656e98257112260ee14239c4
paper_authors.name: Emily Kornman
paper_authors._id: 656e98257112260ee14239c5
paper_authors.name: Daniel Vance
paper_authors._id: 656e98257112260ee14239c6
paper_authors.name: Blair Casey
paper_authors._id: 656e98257112260ee14239c7
paper_authors.name: Steve M. Gleason
paper_authors._id: 656e98257112260ee14239c8
paper_authors.name: Philip Q. Nelson
paper_authors.status: admin_assigned
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paper_authors.fullname: Philip Nelson
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paper_authors.user: blu3r4d0n
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paper_authors.name: Michael P. Brenner
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paper_title: Using Large Language Models to Accelerate Communication for Users with
    Severe Motor Impairments
paper_summary: Finding ways to accelerate text input for individuals with profound motor
  impairments has been a long-standing area of research. Closing the speed gap
  for augmentative and alternative communication (AAC) devices such as
  eye-tracking keyboards is important for improving the quality of life for such
  individuals. Recent advances in neural networks of natural language pose new
  opportunities for re-thinking strategies and user interfaces for enhanced
  text-entry for AAC users. In this paper, we present SpeakFaster, consisting of
  large language models (LLMs) and a co-designed user interface for text entry in
  a highly-abbreviated form, allowing saving 57% more motor actions than
  traditional predictive keyboards in offline simulation. A pilot study with 19
  non-AAC participants typing on a mobile device by hand demonstrated gains in
  motor savings in line with the offline simulation, while introducing relatively
  small effects on overall typing speed. Lab and field testing on two eye-gaze
  typing users with amyotrophic lateral sclerosis (ALS) demonstrated text-entry
  rates 29-60% faster than traditional baselines, due to significant saving of
  expensive keystrokes achieved through phrase and word predictions from
  context-aware LLMs. These findings provide a strong foundation for further
  exploration of substantially-accelerated text communication for motor-impaired
  users and demonstrate a direction for applying LLMs to text-based user
  interfaces.
paper_ai_summary: SpeakFaster uses large language models and a custom interface to reduce motor actions and enhance text entry speed for AAC users, particularly those with ALS.
paper_ai_keywords: large language models LLMs eye-tracking keyboards augmentative and alternative communication (AAC) motor actions predictive keyboards phrase prediction word prediction context-aware LLMs text-entry rates amyotrophic lateral sclerosis (ALS)