Google, Mountain View, CA, USA.
Team Gleason Foundation, New Orleans, LA, USA.
Nat Commun. 2024 Nov 1;15(1):9449. doi: 10.1038/s41467-024-53873-3.
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.
加速辅助性和替代性沟通（AAC）中的文本输入是一个长期存在的研究领域，对严重运动障碍者的生活质量有影响。大型语言模型（LLM）的最新进展为重新思考增强 AAC 中的文本输入策略提供了机会。在本文中，我们提出了 SpeakFaster，它由一个基于 LLM 的用户界面组成，用于以高度缩写的形式进行文本输入，在离线模拟中比传统的预测键盘节省 57%以上的运动操作。一项针对 19 名非 AAC 参与者的移动设备的初步研究表明，与模拟结果一致，打字速度相对较小的变化，实现了运动节省。在两名肌萎缩侧索硬化症的眼动 AAC 用户上进行的实验室和现场测试表明，由于基于 LLM 预测的昂贵按键的显著节省，文本输入速度比基线提高了 29%-60%。这些发现为进一步探索 LLM 辅助的 AAC 和其他用户界面中的文本输入奠定了基础。
