  Prompt-based methods have achieved promising results in most few-shot text
classification tasks. However, for readability assessment tasks, traditional
prompt methods lackcrucial linguistic knowledge, which has already been proven
to be essential. Moreover, previous studies on utilizing linguistic features
have shown non-robust performance in few-shot settings and may even impair
model performance.To address these issues, we propose a novel prompt-based
tuning framework that incorporates rich linguistic knowledge, called Feature
Prompt Tuning (FPT). Specifically, we extract linguistic features from the text
and embed them into trainable soft prompts. Further, we devise a new loss
function to calibrate the similarity ranking order between categories.
Experimental results demonstrate that our proposed method FTP not only exhibits
a significant performance improvement over the prior best prompt-based tuning
approaches, but also surpasses the previous leading methods that incorporate
linguistic features. Also, our proposed model significantly outperforms the
large language model gpt-3.5-turbo-16k in most cases. Our proposed method
establishes a new architecture for prompt tuning that sheds light on how
linguistic features can be easily adapted to linguistic-related tasks.
