Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Revisiting Self-Training for Few-Shot Learning of Language Model   As unlabeled data carry rich task-relevant information, they are proven
useful for few-shot learning of language model. The question is how to
effectively make use of such data. In this work, we revisit the self-training
technique for language model fine-tuning and present a state-of-the-art
prompt-based few-shot learner, SFLM. Given two views of a text sample via weak
and strong augmentation techniques, SFLM generates a pseudo label on the weakly
augmented version. Then, the model predicts the same pseudo label when
fine-tuned with the strongly augmented version. This simple approach is shown
to outperform other state-of-the-art supervised and semi-supervised
counterparts on six sentence classification and six sentence-pair
classification benchmarking tasks. In addition, SFLM only relies on a few
in-domain unlabeled data. We conduct a comprehensive analysis to demonstrate
the robustness of our proposed approach under various settings, including
augmentation techniques, model scale, and few-shot knowledge transfer across
tasks.

26
The categories of this paper are: bayes-an
32
The categories of this paper are: atom-ph
20
The categories of this paper are: acc-phys
13
The categories of this paper are: quant-ph
21
The categories of this paper are: adap-org
15
The categories of this paper are: nucl-ex
22
The categories of this paper are: chao-dyn
8
The categories of this paper are: hep-ex
7
The categories of this paper are: hep-th
19
The categories of this paper are: eess
10
The categories of this paper are: q-bio
29
The categories of this paper are: funct-an
25
The categories of this paper are: patt-sol
2
The categories of this paper are: math
17
The categories of this paper are: q-fin
14
The categories of this paper are: math-ph