Abstract
Text simplification converts complex text into simpler language, improving readability and comprehension. This study evaluates the effectiveness of open-source large language models for text simplification across various categories. We created a dataset of 66,620 lead section pairs from English and Simple English Wikipedia, spanning nine categories, and tested Llama 3 for text simplification. We assessed its output for readability, simplicity, and meaning preservation. Results show improved readability, with simplification varying by category. Texts on Time were the most shortened, while Leisurerelated texts had the greatest reduction of words/characters and syllables per sentence. Meaning preservation was most effective for the Objects and Education categories.

Abstract
Entity linking is an important task in medical natural language processing (NLP) for converting unstructured text into structured data for clinical analysis and semantic interoperability. However, in lower-resource languages, this task is challenging due to the limited availability of domain-specific resources. This paper explores a translation-based cross-lingual entity linking approach using GPT models, GPT-3.5 and GPT-4o, for zero-shot machine translation and entity linking with in-context learning. We evaluate our approach using a Portuguese-English parallel dataset of radiology abstracts. Our results show that chunk-level machine translation outperforms sentence-level translation. Moreover, our translationbased approach to cross-lingual entity linking of UMLS concepts outperformed the multilingual encoder method baseline. However, the in-context learning entity linking approach did not outperform a translation-based approach with a dictionary-based entity linking method.

Abstract
Text readability is vital for effective communication and learning, especially for those with lower information literacy. This research aims to assess Llama 3's ability to grade readability and compare its alignment with established metrics. For that purpose, we create a new dataset of article lead sections from English and Simple English Wikipedia, covering nine categories. The model is prompted to rate the readability of the texts on a grade-level scale, and an in-depth analysis of the results is conducted. While Llama 3 correlates strongly with most metrics, it may underestimate text grade levels.

Abstract
The underrepresentation of women in informatics academia remains a significant issue. In response, Higher Education Institutions have been implementing measures to promote gender balance. To evaluate the prevalence of current recruiting, promotion and retention practices, as well as to identify new strategies, we conducted a survey with 57 respondents, mostly from EU countries. The respondents reported various measures adopted by their Higher Education Institutions (HEI) and the percentages of female representation. Our findings show that women are typically more represented at Ph.D. level compared to senior positions, such as associate or assistant professors. Our study reveals that some institutions are already implementing numerous effective practices to achieve gender balance in the recruitment, retention, and promotion of women in informatics academia, which can serve as exemplary models. Our results provide valuable insights for institutions aiming to enhance gender balance in informatics within academia. © 2025 The Editor(s) (if applicable) and The Author(s).

Abstract
Women are seriously underrepresented in Informatics and STEM areas in general. In this chapter, we raise awareness around some of the key issues and problems that women and other minority groups may face and identify good practices to improve gender balance—and diversity in general—in academia. Our goal is to cover the entire academic career: we start with the recruitment and application evaluation, discuss retainment of female talent, including work life balance, as well as promotion, including strategies to battle the glass ceiling and sticky floor phenomena. In each section, we also provide examples of actions that have been deployed. © 2025 The Editor(s) (if applicable) and The Author(s).

Abstract