Are Prompt-based Models Clueless? (2022)

Abstract: 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.

## introduction finetuning large pre-trained language models with a task-specific head has achieved remarkable performance on many natural language benchmarks @xcite . however, the task-specific head introduces a lot of random taskspecific parameters that require enormous finetuning data to attain optimal performance. the exposure to enormous data increases the potential for models to learn and exploit dataset-specific superficial cues that do not generalize to other datasets without superficial cues @xcite @xcite @xcite . for example, @xcite found that task-specific head models exploit the presence of "not" in the input of argument reasoning comprehension dataset @xcite to achieve state-of-the-art accuracy, but drop to random accuracy when the superficial cue is neutralized. on the other hand, prompting reuses the pre-training language model head, introducing no random task-specific parameters. thus, promptbased models can achieve remarkable performance with only a few training examples @xcite @xcite . hence, few-shot prompting lowers the potential for models to learn and exploit dataset-specific superficial cues. this work empirically investigates whether fewshot prompt-based models exploit superficial cues. specifically, we ask: do few-shot prompt-based models exploit superficial cues? to answer this question, we examine prompted-based models on two fundamental tasks of natural language understanding: natural language inference (nli) and commonsense reasoning; comprehending natural language inference and commonsense are essential to make progress in natural language understanding @xcite @xcite . we analyze the per-formance of prompt-based models trained on the stanford natural language inference dataset @xcite , the multi-genre natural language inference data @xcite , and the choice of plausible alternatives dataset @xcite on instances with and without superficial cues. to facilitate the analysis, we define two types of superficial cues that abstract away from the underlying tasks: context and contextless superficial cues, where the definition of the context depends on the task. for example, in natural language inference tasks, we define the premise as the context, while in multiple-choice tasks, we define the question as the context. context superficial cues such as lexical overlap (figure 1 ) coexist in the context (premise) and the hypothesis. in contrast, contextless superficial cues exist only in the hypothesis (in nli) or in answer choices (in multiple-choice tasks). a dataset can contain either one or both types of superficial cues. therefore, both types must be investigated to sufficiently answer whether a model can exploit superficial cues. as a prerequisite, we reanalyze superficial cues in mnli, snli, and copa datasets to created evaluation sets that have and do not have superficial cues. we find that these datasets contain more superficial cues than previously known. specifically, we find that 90.1% of mnli matched instances contain contextless superficial cues in the hypothesis, while 71.9% snli contains contextless superficial cues. additionally, we find that copa contains not only contextless superficial cues @xcite , but 78.0% of the instances also contain context superficial cues. finally, we examine whether few-shot promptbased models also rely on superficial cues to achieve remarkable performance on mnli, snli, copa, and the hans dataset @xcite . copa experiments reveal that prompt-based models do not rely on contextless superficial cues for typical few-shot training sizes. however, the other empirical results show that prompt-based models heavily rely on superficial cues-failing to generalize to data without superficial cues (figure 1 ). in summary, our contributions are: ## background we will begin with a review of the necessary concepts required to understand the rest of the paper. ## experimental setup the goal of our experimental setup is to answer the following research question: do prompt-based models exploit superficial cues? we decompose this question into two sub-questions: 1) do promptbased models exploit context superficial cues? 2) do prompt-based models exploit contextless superficial cues? training details for all our experiments, we use roberta-large (355m parameters) because it is the widely used model in prompt-based finetuning @xcite @xcite . we build on the source code by @xcite foot_0 and le scao and rush (2021) foot_1 , and we load the pre-trained weights from huggingface @xcite . we use the best-reported hyperparameters and templates (appendix b) from @xcite on nli. all nli models are trained with 16 instances per label. we use the same partitions used by @xcite . on copa we use the best hyperparameters and templates (appendix b) from @xcite ; le scao and rush (2021). we ran all experiments three times with different random seeds and report the average and standard deviation. evaluation the goal of our experimental evaluation is to answer the following question: do prompt-based models exploit superficial cues? we answer this question by investigating whether the model exploits or relies on either context or contextless superficial cues. we train and evaluate our models on english datasets. ## conclusions we presented the first analysis of whether promptbased models exploit superficial cues. we found that prompt-based models exploit superficial cues and fail to generalize well to instances without superficial cues on mnli, snli, copa, and hans. we, further, proposed to divide superficial cues into two: context and contextless superficial cues. our analysis of mnli, snli, and copa has revealed more superficial cues than was previously known.