SinKD:最小化Sinkhorn距离,用于知识蒸
概括
辛克霍恩知识蒸 (SinKD) 通过解决现有的分歧措施的局限性来改善大型语言模型压缩. 在各种自然语言处理任务和模型架构中,SinKD提供了卓越的性能.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 自然语言处理自然语言处理.
背景情况:
- 知识蒸 (KD) 对于压缩大型语言模型 (LLM) 至关重要.
- 使用Kullback-Leibler (KL),逆KL (RKL) 和Jensen-Shannon (JS) 分歧的现有KD方法面临着重叠分布的局限性.
- 这些局限性导致模式平均化,模式崩和模式低估等问题在基于逻辑的KD中.
研究的目的:
- 提出一种新的知识蒸方法,Sinkhorn KD (SinKD),可以克服现有的分歧措施的局限性.
- 提高教师和学生模型之间的分布差异评估的精度和细微差别.
- 提高KD对各种自然语言处理 (NLP) 任务的有效性.
主要方法:
- 利用Sinkhorn距离来准确评估教师和学生模型之间的分布差异.
- 引入了KD的批量重制,超越了样本限制.
- 在高维空间中捕获样本分布的几何复杂性.
主要成果:
- 在GLUE和SuperGLUE基准指标上表现出优于最先进的 (SOTA) 方法的优势.
- 通过仅用编码器,编码器-解码器和仅用解码器架构在LLM中实现了显著的改进.
- 验证了拟议的SinKD方法的可比性,有效性和通用性.
结论:
- 与传统的分歧措施相比,SinKD为基于逻辑的KD提供了更有效的方法.
- 批量智能重制和Sinkhorn距离能够实现强大的模型压缩.
- SinKD代表了从大型语言模型中有效地提炼知识的重大进步.
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