  Large Language Models have been successful in a wide variety of Natural
Language Processing tasks by capturing the compositionality of the text
representations. In spite of their great success, these vector representations
fail to capture meaning of idiomatic multi-word expressions (MWEs). In this
paper, we focus on the detection of idiomatic expressions by using binary
classification. We use a dataset consisting of the literal and idiomatic usage
of MWEs in English and Portuguese. Thereafter, we perform the classification in
two different settings: zero shot and one shot, to determine if a given
sentence contains an idiom or not. N shot classification for this task is
defined by N number of common idioms between the training and testing sets. In
this paper, we train multiple Large Language Models in both the settings and
achieve an F1 score (macro) of 0.73 for the zero shot setting and an F1 score
(macro) of 0.85 for the one shot setting. An implementation of our work can be
found at
https://github.com/ashwinpathak20/Idiomaticity_Detection_Using_Few_Shot_Learning.
