Recent approaches have explored language-guided classifiers capable of
classifying examples from novel tasks when provided with task-specific natural
language explanations, instructions or prompts (Sanh et al., 2022; R. Menon et
al., 2022). While these classifiers can generalize in zero-shot settings, their
task performance often varies substantially between different language
explanations in unpredictable ways (Lu et al., 2022; Gonen et al., 2022). Also,
current approaches fail to leverage unlabeled examples that may be available in
many scenarios. Here, we introduce TALC, a framework that uses data programming
to adapt a language-guided classifier for a new task during inference when
provided with explanations from multiple teachers and unlabeled test examples.
Our results show that TALC consistently outperforms a competitive baseline from
prior work by an impressive 9.3% (relative improvement). Further, we
demonstrate the robustness of TALC to variations in the quality and quantity of
provided explanations, highlighting its potential in scenarios where learning
from multiple teachers or a crowd is involved. Our code is available at:
this https URL

介绍了一种使用数据编程的框架 TALC，该框架利用来自多个教师的解释和未标记的测试样本，在推理过程中对新任务的语言引导分类器进行自适应。结果表明 TALC 在相对改进方面始终优于先前工作中的竞争基线达 9.3％。此外，我们展示了 TALC 对提供的解释质量和数量变化的鲁棒性，凸显了它在涉及多个教师或群体学习的场景中的潜力。

利用多个教师进行语言引导分类器的测试时间自适应

Leveraging Multiple Teachers for Test-Time Adaptation of Language-Guided  Classifiers

In few-shot classification, we are interested in learning algorithms that
train a classifier from only a handful of labeled examples. Recent progress in
few-shot classification has featured meta-learning, in which a parameterized
model for a learning algorithm is defined and trained on episodes representing
different classification problems, each with a small labeled training set and
its corresponding test set. In this work, we advance this few-shot
classification paradigm towards a scenario where unlabeled examples are also
available within each episode. We consider two situations: one where all
unlabeled examples are assumed to belong to the same set of classes as the
labeled examples of the episode, as well as the more challenging situation
where examples from other distractor classes are also provided. To address this
paradigm, we propose novel extensions of Prototypical Networks (Snell et al.,
2017) that are augmented with the ability to use unlabeled examples when
producing prototypes. These models are trained in an end-to-end way on
episodes, to learn to leverage the unlabeled examples successfully. We evaluate
these methods on versions of the Omniglot and miniImageNet benchmarks, adapted
to this new framework augmented with unlabeled examples. We also propose a new
split of ImageNet, consisting of a large set of classes, with a hierarchical
structure. Our experiments confirm that our Prototypical Networks can learn to
improve their predictions due to unlabeled examples, much like a
semi-supervised algorithm would.

本研究致力于发展一种在少量标记数据情况下对未标记数据进行分类的方法并提出一种新型 Prototypical Networks 和一种使用未标记数据的元学习算法来解实际问题，经过对 Omniglot、miniImageNet 和 ImageNet 进行实验，验证了这些算法可以使预测结果得到显著改进。