你又打断了我!:通过逐步澄清,使语音助理更加适合痴呆症
Angus Addlesee1, Arash Eshghi1,2
1Interaction Lab, Heriot-Watt University, Edinburgh, United Kingdom.
Frontiers in dementia
|July 31, 2024
概括
大型语言模型 (LLM) 可以通过生成增量澄清请求 (iCRs) 来处理用户暂停来改善语音助理. 更大的模型和特定的提示增强了他们支持自然对话的能力,特别是对于痴呆症患者.
科学领域:
- 计算语言学 计算语言学
- 人与计算机的交互
- 人工智能的人工智能
背景情况:
- 自发对话涉及渐进式规划,导致停顿和截断的发言.
- 现有的语音助理在用户暂停时遇到困难,导致互动中断,特别是在痴呆症患者中.
- 增量澄清请求 (iCRs) 对于自然和强大的语音助理交互至关重要.
研究的目的:
- 建立澄清请求作为自发言语中断后恢复的有效策略.
- 介绍和分析SLUICE-CR,这是人类生产的新型iCRs.
- 使用本语料库,评估大型语言模型 (LLM) 的增量处理能力.
主要方法:
- 利用现有的截断语句库来验证澄清请求.
- 收集并分析了SLUICE-CR,这是一个由3000个众包iCR组成的新集体.
- 通过评估iCR生成质量和iCR后的响应准确度,探测了LLM增量处理.
主要成果:
- 只有在较大的模型大小和基于示例的提示的情况下,LLM才能产生适合环境的iCR.
- 较大的LLM证明了对增量澄清交流的优越解释.
- 自动回归语言模型显示了增量语言理解和生成的固有能力.
结论:
- 可以配置LLM来更好地处理痴呆症患者常见的语言现象,从而提高可访问性.
- 通过使用iCR功能来增强语音助理,可以减轻用户的丧和交互失败.
- 这项研究为更自然,更强大的人类-人工智能对话代理铺平了道路.
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