Do Prompt-Based Models Really Understand the Meaning of their Prompts?
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Recently, a boom of papers have shown extraordinary progress in few-shot learning with various prompt-based models. Such success can give the impression that prompts help models to learn faster in the same way that humans learn faster when provided with task instructions expressed in natural language. In this study, we experiment with over 30 prompts manually written for natural language inference (NLI). We find that models learn just as fast with many prompts that are intentionally irrelevant or even pathologically misleading as they do with instructively "good" prompts. Additionally, we find that model performance is more dependent on the choice of the LM target words (a.k.a. the "verbalizer" that converts LM vocabulary prediction to class labels) than on the text of the prompt itself. In sum, we find little evidence that suggests existing prompt-based models truly understand the meaning of their given prompts.
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