  Finetuning large pre-trained language models with a task-specific head has
advanced the state-of-the-art on many natural language understanding
benchmarks. However, models with a task-specific head require a lot of training
data, making them susceptible to learning and exploiting dataset-specific
superficial cues that do not generalize to other datasets. Prompting has
reduced the data requirement by reusing the language model head and formatting
the task input to match the pre-training objective. Therefore, it is expected
that few-shot prompt-based models do not exploit superficial cues. This paper
presents an empirical examination of whether few-shot prompt-based models also
exploit superficial cues. Analyzing few-shot prompt-based models on MNLI, SNLI,
HANS, and COPA has revealed that prompt-based models also exploit superficial
cues. While the models perform well on instances with superficial cues, they
often underperform or only marginally outperform random accuracy on instances
without superficial cues.
