It has been experimentally demonstrated that humans are able to learn in a
manner that allows them to make predictions on categories for which they have
not seen any examples (Malaviya et al., 2022). Sucholutsky and Schonlau (2020)
have recently presented a machine learning approach that aims to do the same.
They utilise synthetically generated data and demonstrate that it is possible
to achieve sub-linear scaling and develop models that can learn to recognise N
classes from M training samples where M is less than N - aka less-than-one shot
learning. Their method was, however, defined for univariate or simple
multivariate data (Sucholutsky et al., 2021). We extend it to work on large,
high-dimensional and real-world datasets and empirically validate it in this
new and challenging setting. We apply this method to learn previously unseen
NLP tasks from very few examples (4, 8 or 16). We first generate compact,
sophisticated less-than-one shot representations called soft-label prototypes
which are fitted on training data, capturing the distribution of different
classes across the input domain space. We then use a modified k-Nearest
Neighbours classifier to demonstrate that soft-label prototypes can classify
data competitively, even outperforming much more computationally complex
few-shot learning methods.

本文介绍一种使用合成数据进行少样本分类的方法，该方法可以分类高维真实世界数据集，并将其应用于以前未见的自然语言处理任务中，使用软标签原型和修改的 k 近邻分类器进行分类，结果表明该方法可以在少数样本下进行分类，甚至超越了更复杂的 few-shot 学习方法。

利用软标签原型从少量示例中学习新任务

Learning New Tasks from a Few Examples with Soft-Label Prototypes

Being able to learn from small amounts of data is a key characteristic of
human intelligence, but exactly {\em how} small? In this paper, we introduce a
novel experimental paradigm that allows us to examine classification in an
extremely data-scarce setting, asking whether humans can learn more categories
than they have exemplars (i.e., can humans do "less-than-one shot" learning?).
An experiment conducted using this paradigm reveals that people are capable of
learning in such settings, and provides several insights into underlying
mechanisms. First, people can accurately infer and represent high-dimensional
feature spaces from very little data. Second, having inferred the relevant
spaces, people use a form of prototype-based categorization (as opposed to
exemplar-based) to make categorical inferences. Finally, systematic,
machine-learnable patterns in responses indicate that people may have efficient
inductive biases for dealing with this class of data-scarce problems.

本文通过创新实验法，探究了人类对于数据稀少情境下的学习能力，发现人类能够通过很少量的数据来得到高维特征空间，使用基于原型的分类方式进行分类，并且能够有效地处理类似的缺少数据的问题。