第一步是最困难的:用于大型语言模型的时间数据表示和标记化的陷
Dimitris Spathis1,2, Fahim Kawsar1,3
1Nokia Bell Labs, Cambridge, CB3 0FA, United Kingdom.
概括
大型语言模型 (LLM) 难以处理时间数据,因为代码化器不是为数字输入而设计的. 解决这种输入表示对于有效的LLM应用与时间序列数据至关重要.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 数据科学数据科学数据科学
背景情况:
- 大型语言模型 (LLM) 显示先进的推理,使它们适合作为个人助理.
- 整合数字和时间数据,例如来自可穿戴设备或电子健康记录的数据,对LLMs来说是一个重大挑战.
- 为文本设计的LLM代币化器经常无法捕捉数值和时间模式的细微差别,导致"模式差距".
研究的目的:
- 调查代表和令牌化的时间数据的挑战,为LLMs.
- 分析当前用于将时间序列数据输入到LLMs的方法的有效性.
- 用时间数据提出改善LLM绩效的方向.
主要方法:
- 在移动传感数据集上使用OpenAItokenizer进行了一项案例研究.
- 进行了对最近关于将时间序列数据输入到以人为中心的任务的LLM的文献的审查.
- 研究了诸如零射击提示和少数射击学习之类的实验设置.
主要成果:
- 案例研究表明,LLM将时间和传感器值标记为无意义的单位,无法理解时间关系.
- 现有的研究严重依赖于快速的工程和数据聚合,以弥合模式差距.
- 对于时间数据,模型表达性 (域特定模型) 和参数效率 (零射击LLM) 之间存在一个权衡.
结论:
- 目前的LLM代币化器没有针对数值数据进行优化,培训数据中的有限时间序列示例加剧了这个问题.
- 平衡模型表达性和计算效率是有效的时间数据集成的关键.
- 快速调整,模型移植和增强的令牌化器是未来研究的有希望的途径,特别是在生物医学信号处理领域.
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