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Through in-context learning (ICL), large-scale language models are effectivefew-shot learners without additional model fine-tuning. However, the ICLperformance does not scale well with the number of available training samplesas it is limited by the inherent input length constraint of the underlyinglanguage model. Meanwhile, many studies have revealed that language models arealso powerful feature extractors, allowing them to be utilized in a black-boxmanner and enabling the linear probing paradigm, where lightweightdiscriminators are trained on top of the pre-extracted input representations.This paper proposes prompt-augmented linear probing (PALP), a hybrid of linearprobing and ICL, which leverages the best of both worlds. PALP inherits thescalability of linear probing and the capability of enforcing language modelsto derive more meaningful representations via tailoring input into a moreconceivable form. Throughout in-depth investigations on various datasets, weverified that PALP significantly enhances the input representations closing thegap between ICL in the data-hungry scenario and fine-tuning in thedata-abundant scenario with little training overhead, potentially making PALP astrong alternative in a black-box scenario.

Prompt-Augmented Linear Probing: Scaling beyond the Limit of Few-shot In-Context Learners