Models based on the Transformer neural network architecture have seen success
on a wide variety of tasks that appear to require complex "cognitive branching"
-- or the ability to maintain pursuit of one goal while accomplishing others.
In cognitive neuroscience, success on such tasks is thought to rely on
sophisticated frontostriatal mechanisms for selective \textit{gating}, which
enable role-addressable updating -- and later readout -- of information to and
from distinct "addresses" of memory, in the form of clusters of neurons.
However, Transformer models have no such mechanisms intentionally built-in. It
is thus an open question how Transformers solve such tasks, and whether the
mechanisms that emerge to help them to do so bear any resemblance to the gating
mechanisms in the human brain. In this work, we analyze the mechanisms that
emerge within a vanilla attention-only Transformer trained on a simple sequence
modeling task inspired by a task explicitly designed to study working memory
gating in computational cognitive neuroscience. We find that, as a result of
training, the self-attention mechanism within the Transformer specializes in a
way that mirrors the input and output gating mechanisms which were explicitly
incorporated into earlier, more biologically-inspired architectures. These
results suggest opportunities for future research on computational similarities
between modern AI architectures and models of the human brain.

通过分析基于 Transformer 神经网络架构的模型在简单序列建模任务上的训练结果，发现 Transformer 内部的自注意机制在一定程度上呈现出与输入和输出分离机制相似的特征，暗示了未来关于现代 AI 架构与人脑模型之间计算上的相似性的研究机会。

Transformer 机制在训练人类工作记忆任务时模拟前额 - 纹状体门控操作

Transformer Mechanisms Mimic Frontostriatal Gating Operations When  Trained on Human Working Memory Tasks

Large language models (LLMs) and generative AI have played a transformative
role in computer research and applications. Controversy has arisen as to
whether these models output copyrighted data, which can occur if the data the
models are trained on is copyrighted. LLMs are built on the transformer neural
network architecture, which in turn relies on a mathematical computation called
Attention that uses the softmax function.
In this paper, we show that large language model training and optimization
can be seen as a softmax regression problem. We then establish a method of
efficiently performing softmax regression, in a way that prevents the
regression function from generating copyright data. This establishes a
theoretical method of training large language models in a way that avoids
generating copyright data.

利用训练大语言模型的理论方法，可以避免生成版权数据。