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How Many Data Points is a Prompt Worth?
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When fine-tuning pretrained models for classification, researchers either use a generic model head or a task-specific prompt for prediction. Proponents of prompting have argued that prompts provide a method for injecting task-specific guidance, which is beneficial in low-data regimes. We aim to quantify this benefit through rigorous testing of prompts in a fair setting: comparing prompted and head-based fine-tuning in equal conditions across many tasks and data sizes. By controlling for many sources of advantage, we find that prompting does indeed provide a benefit, and that this benefit can be quantified per task. Results show that prompting is often worth 100s of data points on average across classification tasks.
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Cited by 1 Pith paper
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On the Privacy Risk of In-context Learning
A confidence-based membership inference attack identifies prompt demonstration data with AUC 0.69-0.86, more than fine-tuned models leak at matched utility, and ensembling reduces this to near random.
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