Manual annotation of pragmastylistic features in sensationalist media is a resource-intensive bottleneck for corpus- based research, particularly for lower-resource languages. This paper evaluates whether Large Language Models (LLMs) can reliably automate this process. We benchmark two proprietary models, OpenAI’s GPT-5 and Google’s Gemini 2.5 Pro, on annotating eight sensationalist linguistic and orthographic features within a corpus of 508 Serbian celebrity magazine headlines. Our methodology involves a systematic comparison of five prompting strategies: zero-shot, few-shot (1, 3, and 5 examples), and chain-of-thought. Results demonstrate that LLMs can achieve high alignment with a manually curated gold standard, reaching a peak macro-F1 score of 98.76%. Notably, the most effective and cost-efficient configuration was GPT-5 using a simple zero-shot prompt. Qualitative error analysis reveals that remaining inaccuracies are systematic, primarily involving pragmatic conventions, discourse scope, and quoted speech. We conclude that LLMs are viable for first-pass annotation of well-defined features in Serbian, though implicit and genre-dependent cues require further study. To support reproducibility and future research on underrepresented languages, we provide our full prompting setup, evaluation procedures, and a detailed cost comparison.

Humor detection in low-resource languages is hampered by cultural nuance and subjective annotation. We test two large language models, GPT-4 and Gemini 2.5 Flash, on labeling humor in 6,000 Croatian tweets with expert gold labels generated through a rigorous annotation pipeline. LLM–human agreement (κ = 0.28) matches human–human agreement (κ = 0.27), while LLM–LLM agreement is substantially higher (κ = 0.63). Although concordance with expert adjudication is lower, additional metrics imply that the models equal a second human annotator while working far faster and at negligible cost. These findings suggest, even with simple prompting, LLMs can efficiently bootstrap subjective datasets and serve as practical annotation assistants in linguistically under-represented settings.

This paper provides an overview of the main achievements of the completed PRINCIPLE project, a 2-year action funded by the European Commission under the Connecting Europe Facility (CEF) programme. PRINCIPLE focused on collecting high-quality language resources for Croatian, Icelandic, Irish and Norwegian, which are severely low-resource languages, especially for building effective machine translation (MT) systems. We report the achievements of the project, primarily, in terms of the large amounts of data collected for all four low-resource languages and of promoting the uptake of neural MT (NMT) for these languages.

PRINCIPLE was a Connecting Europe Facility (CEF)-funded project that focused on the identification, collection and processing of language resources (LRs) for four European under-resourced languages (Croatian, Icelandic, Irish and Norwegian) in order to improve translation quality of eTranslation, an online machine translation (MT) tool provided by the European Commission. The collected LRs were used for the development of neural MT engines in order to verify the quality of the resources. For all four languages, a total of 66 LRs were collected and made available on the ELRC-SHARE repository under various licenses. For Croatian, we have collected and published 20 LRs: 19 parallel corpora and 1 glossary. The majority of data is in the general domain (72 % of translation units), while the rest is in the eJustice (23 %), eHealth (3 %) and eProcurement (2 %) Digital Service Infrastructures (DSI) domains. The majority of the resources were for the Croatian-English language pair. The data was donated by six data contributors from the public as well as private sector. In this paper we present a subset of 13 Croatian LRs developed based on public administration documents, which are all made freely available, as well as challenges associated with the data collection, cleaning and processing.

This paper updates the progress made on the PRINCIPLE project, a 2-year action funded by the European Commission under the Connecting Europe Facility (CEF) programme. PRINCIPLE focuses on collecting high-quality language resources for Croatian, Icelandic, Irish and Norwegian, which have been identified as low-resource languages, especially for building effective machine translation (MT) systems. We report initial achievements of the project and ongoing activities aimed at promoting the uptake of neural MT for the low-resource languages of the project.