We investigate how gender authorship influences polar, i.e. positive and negative gender reference. Given German-language newspaper texts where the full name of the authors are known and their gender can be inferred from the first names. And given that nouns in the text have gender reference, i.e. are labeled by a gender classifier as female or male denoting nouns. If these nouns carry a polar load, they count towards the gender-specific statistics we are interested in. A polar load is given either via phrase-level sentiment composition, or by a verb-based analysis of the polar role a noun (phrase) plays: is it framed by the verb as a positive or negative actor, or as receiving a positive or negative effect? Also, reported gender-gender relations (in favor, against) might be gender-specific. Statistical hypothesis testing is carried out in order to find out whether significant gender-wise correlations exist. We found that, in fact, gender reference is gender-specific: each gender significantly more often focuses on their own gender than the other one and e.g. positive actorship supremacy is claimed (intra-) gender-wise.

Large language models (LLMs) demonstrate exceptional proficiency in both the comprehension and generation of textual data, particularly in English, a language for which extensive public benchmarks have been established across a wide range of natural language processing (NLP) tasks. Nonetheless, their performance in multilingual contexts and specialized domains remains less rigorously validated, raising questions about their reliability and generalizability across linguistically diverse and domain-specific settings. The second edition of the Shared Task on Multilingual Multitask Information Retrieval aims to provide a comprehensive and inclusive multilingual evaluation benchmark which aids assessing the ability of multilingual LLMs to capture logical, factual, or causal relationships within lengthy text contexts and generate language under sparse settings, particularly in scenarios with under-resourced languages. The shared task consists of two subtasks crucial to information retrieval: Named entity recognition (NER) and reading comprehension (RC), in 7 data-scarce languages: Azerbaijani, Swiss German, Turkish and , which previously lacked annotated resources in information retrieval tasks. This year specifally focus on the multiple-choice question answering evaluation setting which provides a more objective setting for comparing different methods across languages.

In this short paper, we combine the semantic perspective of particular verbs as casting a positive or negative relationship between their role fillers with a pragmatic examination of how the distribution of particular vulnerable role filler subtypes (children, migrants, etc.) looks like. We focus on the gender subtype and strive to extract gender-specific semantic role profiles: who are the predominant sources and targets of which polar events - men or women. Such profiles might reveal gender stereotypes or biases (of the media), but as well could be indicative of our social reality.