  Through in-context learning (ICL), large-scale language models are effective
few-shot learners without additional model fine-tuning. However, the ICL
performance does not scale well with the number of available training samples
as it is limited by the inherent input length constraint of the underlying
language model. Meanwhile, many studies have revealed that language models are
also powerful feature extractors, allowing them to be utilized in a black-box
manner and enabling the linear probing paradigm, where lightweight
discriminators are trained on top of the pre-extracted input representations.
This paper proposes prompt-augmented linear probing (PALP), a hybrid of linear
probing and ICL, which leverages the best of both worlds. PALP inherits the
scalability of linear probing and the capability of enforcing language models
to derive more meaningful representations via tailoring input into a more
conceivable form. Throughout in-depth investigations on various datasets, we
verified that PALP significantly enhances the input representations closing the
gap between ICL in the data-hungry scenario and fine-tuning in the
data-abundant scenario with little training overhead, potentially making PALP a
strong alternative in a black-box scenario.
