From being a niche technology with practical applications in translation and speech recognition, NLP is now underpinning the AI era through LLMs, promising a universal economic impact in the future. Although transitioning to the AI era is hyped by BigTech companies, practical adoption of the LLM capabilities for economically impactful tasks and processes goes via education of specialists capable to apply it properly. Human-in-the-loop, accuracy measurement, fine-tuning, on-premises processing of sensitive data have become essential skills for applying NLP. This short paper introduces two language technology modules developed and piloted at the Faculty of Science and Technology of the University of Latvia.

This short paper provides an overview of language technology related modules and courses developed at three leading universities of Latvia - University of Latvia (UL), Riga Technical University (RTU) and Riga Stradiņš University (RSU).

Potentially idiomatic expressions (PIEs) carry meanings inherently tied to the everyday experience of a given language community. As such, they constitute an interesting challenge for assessing the linguistic (and to some extent cultural) capabilities of NLP systems. In this paper, we present XMPIE, a parallel multilingual and multimodal dataset of potentially idiomatic expressions. The dataset, containing 34 languages and over ten thousand items, allows comparative analyses of idiomatic patterns among language-specific realisations and preferences in order to gather insights about shared cultural aspects. This parallel dataset allows evaluation of language model performance for a given PIE in different languages and whether idiomatic understanding in one language can be transferred to another. Moreover, the dataset supports the study of PIEs across textual and visual modalities, to measure to what extent PIE understanding in one modality transfers or implies in understanding in another modality (text vs. image). The data was created by language experts, with both textual and visual components crafted under multilingual guidelines, and each PIE is accompanied by five images representing a spectrum from idiomatic to literal meanings, including semantically related and random distractors. The result is a high-quality benchmark for evaluating multilingual and multimodal idiomatic language understanding.