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Vast efforts have been devoted to creating high-performance few-shotlearners, i.e., large-scale pretrained language models (PLMs) that perform wellwith little downstream task training data. Training PLMs has incurredsignificant cost, but utilizing the few-shot learners is still challenging dueto their enormous size. This work focuses on a crucial question: How to makeeffective use of these few-shot learners? We propose LMTurk, a novel approachthat treats few-shot learners as crowdsourcing workers. The rationale is thatcrowdsourcing workers are in fact few-shot learners: They are shown a fewillustrative examples to learn about a task and then start annotating. LMTurkemploys few-shot learners built upon PLMs as workers. We show that theresulting annotations can be utilized to train models that solve the task welland are small enough to be deployable in practical scenarios. Active learningis integrated into LMTurk to reduce the amount of queries made to PLMs,minimizing the computational cost of running PLM inference passes. Altogether,LMTurk is an important step towards making effective use of current PLMs.

LMTurk: Few-Shot Learners as Crowdsourcing Workers in a Language-Model-as-a-Service Framework