使用轻量级的LLM来预测设备上的查询意图,以支持无处不在的对话
Mateusz Dubiel1, Yasmine Barghouti1, Kristina Kudryavtseva1
1University of Luxembourg, 4365, Esch-sur-Alzette, Luxembourg.
Scientific reports
|June 3, 2024
概括
这项研究为设备上的对话代理 (CA) 微调了轻量级的大型语言模型 (LLM),增强了隐私和灵活性. RoBERTa 和 XLNet 模型实现了与 ChatGPT 相当的性能,同时保持了较小的内存占用量.
科学领域:
- 人工智能的人工智能
- 自然语言处理自然语言处理.
- 人与计算机的交互
背景情况:
- 目前的对话代理 (CAs) 依赖于严格的,基于规则的对话模型,限制了灵活性和可扩展性.
- 大型语言模型 (LLM) 提供了高级功能,但通常需要云部署,从而引起最终用户的隐私问题.
- 需要在设备上的LLM来实现个性化,无处不在和保护隐私的对话性AI.
研究的目的:
- 调查轻量级预训练LLM的微调,以在CA中进行设备内意图预测.
- 评估LLM性能,内存足迹和移动和边缘应用程序的隐私影响.
- 确定最佳的LLM架构,平衡性能和资源限制.
主要方法:
- 利用转移学习来微调轻量级预训练的LLM,特别是RoBERTa和XLNet.
- 专注于用于CA对话管理的用户查询意图预测.
- 进行实验,以评估模型性能与ChatGPT等基准进行比较,并评估内存使用情况.
主要成果:
- 精心调整的RoBERTa和XLNet模型显示出强大的意图预测能力,用于设备上的CA.
- 这些模型的性能与ChatGPT相当,尽管它们的尺寸显著更小.
- 选择的LLM提供了性能,内存足迹和适合在设备上部署之间的有利权衡.
结论:
- 轻量级的LLM,当微调时,非常适合在设备上的对话代理,解决隐私和可扩展性的限制.
- 罗伯塔和XLNet为开发个性化,保护隐私的CA提供了一个实用的解决方案.
- 这项研究为利益相关者提供了宝贵的见解,以平衡LLM能力与部署约束.
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