Do Prompt-Based Models Really Understand the Meaning of Their Prompts?
Do Prompt-Based Models Really Understand the Meaning of Their Prompts?
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DOI：
10.18653/v1/2022.naacl-main.167
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发表时间：
2021-09
期刊：
影响因子：
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通讯作者：
Albert Webson;Ellie Pavlick
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文献类型：
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作者：
Albert Webson;Ellie Pavlick
最近，一系列论文在零射击中表现出了非凡的进步，并且通过各种及时的模型进行了少量的学习。用自然语言表示。许多提示在有意义的“良好”提示中有意无关紧要，甚至在病理上误导了。经过数百个提示的训练模型（Sanh等，2021）。零镜头，尽管有迅速的模型的令人印象深刻的改进，但我们发现了严重限制的证据，这些局限性质疑了这种改进的程度，从模型中得出了与人类对任务指令相似的方式理解任务指令的程度。
Recently, a boom of papers has shown extraordinary progress in zero-shot and few-shot learning with various prompt-based models. It is commonly argued 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 can learn just as fast with many prompts that are intentionally irrelevant or even pathologically misleading as they do with instructively “good” prompts. Further, such patterns hold even for models as large as 175 billion parameters (Brown et al., 2020) as well as the recently proposed instruction-tuned models which are trained on hundreds of prompts (Sanh et al., 2021). That is, instruction-tuned models often produce good predictions with irrelevant and misleading prompts even at zero shots. In sum, notwithstanding prompt-based models’ impressive improvement, we find evidence of serious limitations that question the degree to which such improvement is derived from models understanding task instructions in ways analogous to humans’ use of task instructions.