Since 1975, research on MD is ongoing because “the task is not considered solved” (Dankin, Bar, and Dershowitz 2022, 125). The methodologies applied to MD have evolved with the development of computational capabilities starting with rule-based, statistical and machine learning methods (Ptiček and Dobša 2023). The current state-of-the-art method for MD involves utilizing LLMs, often derivations of the BERT model (Devlin et al. 2019), which are based on the Transformer architecture by Vaswani et al. (2017) that allows to fine-tune an existing language model on a specific downstream task like MD (Babieno et al. 2022; Li et al. 2023; Z. Song et al. 2024). Recently, prompt engineering started to be utilized for MD. Instead of labelled data this method uses task-specific formulated prompts (Jia and Li 2024). Chen et al. (2024) expanded the task to include metaphor reason in addition to detection since they found that methods focusing solely on a metaphorical versus literal distinction did not generalise well. Another recent development is to detect metaphor in multi-modal settings, such as memes, where the classification task includes both texts and images (Xu et al. 2024).