太大而不能失败:较大的语言模型对诱导与痴呆相关的语言异常具有不成比例的弹性
Changye Li1, Zhecheng Sheng1, Trevor Cohen2
1University of Minnesota.
概括
较大的人工神经网络对模拟的神经退行产生更大的弹性,类似于人类的认知储备. 这表明变压器注意力机制可以模拟衰老和神经退行性疾病的进展.
科学领域:
- 人工智能的人工智能
- 计算神经科学是一种神经科学.
- 神经退行性疾病研究研究
背景情况:
- 了解复杂的人工神经网络 (ANN) 是至关重要的,特别是在医疗保健.
- 困惑度 (PPL) 衡量神经语言模型 (NLM) 对新数据的惊程度.
- 以前的研究将NLM困惑性变化与阿尔茨海默病中的语言异常联系起来.
研究的目的:
- 在变压器模型中探索一种新的双向注意力头部切除方法.
- 调查ANN是否表现出类似于认知和大脑储备的特性.
- 模拟神经退行性疾病进展和衰老的各个方面.
主要方法:
- 在基于变压器的NLM (GPT-2) 上使用双向注意力头移除技术.
- 评估模型性能恶化,以掩盖/剥离注意力头的比例为基础.
- 在不同尺寸的GPT-2模型中比较结果.
主要成果:
- 较大的GPT-2模型需要不成比例地更高的注意力头被切除,以显示与较小的模型相比,类似的性能降低.
- 这表明较大的模型具有更大的弹性.
结论:
- 变压器模型中的注意力机制可以作为认知和大脑储备的模拟.
- 这种方法有可能用于模拟神经退行性疾病和衰老的各个方面.
- 特别是较大的ANN,表现出一种类似于人类大脑储备的弹性.
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