5 citations · 8 across the 7 of their papers we have counts for
6 papers · 1 filter
Analysing Self-Harm Representations in Language Models: a Cross-Architecture Study
Luis Espinosa-Anke, Carla Perez-Almendros
Self-harm content is particularly challenging to detect using NLP techniques, and is also a high-stakes task which requires the highest accuracy to enable timely intervention or fl…
SemEval-2026 Task 7: Everyday Knowledge Across Diverse Languages and Cultures
Nedjma Ousidhoum, Junho Myung, Carla Perez-Almendros +27
We present our shared task on evaluating the adaptability of LLMs and NLP systems across multiple languages and cultures. The task data consist of an extended version of our manual…
Why are all LLMs Obsessed with Japanese Culture? On the Hidden Cultural and Regional Biases of LLMs
Joseba Fernandez de Landa, Carla Perez-Almendros, Jose Camacho-Collados
LLMs have limitations when it comes to cultural coverage and competence, and in some cases, show specific cultural biases. Although prior studies have examined the cultural capabil…
Sensitive Content Classification in Social Media: A Holistic Resource and Evaluation
Dimosthenis Antypas, Indira Sen, Carla Perez-Almendros +2
The detection of sensitive content in large datasets is crucial for ensuring that shared and analysed data is free from harmful material. However, current moderation tools, such as…
BLEnD: A Benchmark for LLMs on Everyday Knowledge in Diverse Cultures and Languages
Junho Myung, Nayeon Lee, Yi Zhou +19
Large language models (LLMs) often lack culture-specific knowledge of daily life, especially across diverse regions and non-English languages. Existing benchmarks for evaluating LL…
Don't Patronize Me! An Annotated Dataset with Patronizing and Condescending Language towards Vulnerable Communities
Carla Pérez-Almendros, Luis Espinosa-Anke, Steven Schockaert
In this paper, we introduce a new annotated dataset which is aimed at supporting the development of NLP models to identify and categorize language that is patronizing or condescend…