  Attribution scores indicate the importance of different input parts and can,
thus, explain model behaviour. Currently, prompt-based models are gaining
popularity, i.a., due to their easier adaptability in low-resource settings.
However, the quality of attribution scores extracted from prompt-based models
has not been investigated yet. In this work, we address this topic by analyzing
attribution scores extracted from prompt-based models w.r.t. plausibility and
faithfulness and comparing them with attribution scores extracted from
fine-tuned models and large language models. In contrast to previous work, we
introduce training size as another dimension into the analysis. We find that
using the prompting paradigm (with either encoder-based or decoder-based
models) yields more plausible explanations than fine-tuning the models in
low-resource settings and Shapley Value Sampling consistently outperforms
attention and Integrated Gradients in terms of leading to more plausible and
faithful explanations.
