Supervision versus Demonstration-Based In-Context Learning for Multiword Expression Classification

Language is full of expressions whose meaning can't be derived from their parts — idioms, fixed phrases, and culturally embedded constructions that trip up both learners and machines. Turkish presents a particularly interesting case, where idiomatic verb constructions are surface-identical to their literal counterparts. Understanding these distinctions matters for machine translation, language learning applications, legal document parsing, and sentiment analysis. This paper explores whether prompting large language models with examples can match or outperform dedicated supervised classifiers, with nuanced findings about how demonstrations can both help and mislead. The results have broad relevance for low-resource languages seeking to leverage large multilingual models. Authors: Sercan Karakaş, Yusuf Şimşek Paper: https://arxiv.org/abs/2606.07479v1