在智能手机上使用LLM Powered文本输入解码和灵活打字
Yan Ma1, I V Ramakrishnan1, Dan Zhang1
1Department of Computer Science, Stony Brook University, Stony Brook, New York, USA.
概括
大型语言模型 (LLM) 提高键盘解码精度. 一个微调的FLAN-T5模型可以实现灵活的打字,将点击和手势结合起来,以改善用户体验和多样化的输入偏好.
科学领域:
- 自然语言处理自然语言处理.
- 人与计算机的交互
背景情况:
- 大型语言模型 (LLM) 在语言任务中表现出色,但在键盘解码中未得到充分利用.
- 键盘解码将用户输入如点击和手势转换为文本.
研究的目的:
- 开发和评估一种基于LLM的新型解码器,用于键盘输入.
- 引入并评估结合各种输入方式的灵活类型化方法的有效性.
主要方法:
- 微调FLAN-T5模型用于键盘解码任务.
- 评估用户绘制的手势和真实世界的键入数据的性能.
- 进行一项用户研究,以评估灵活打字方法.
主要成果:
- FLAN-T5解码器在手势上达到93.1%的准确性,在点击打字上达到95.4%的准确性.
- 灵活的打字使用了字体手势 (35.9%),点击 (29.0%),多动作手势 (6.1%) 和点击手势 (29.0%).
- 基于LLM的解码器在准确性方面超过了现有的手势解码器.
结论:
- 基于LLM的解码器为键盘输入提供了卓越的准确性.
- 灵活的打字增强了用户体验,并适应了各种输入偏好.
- 这种方法推进了人机交互领域的文本输入.
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