6 papers
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 been showing limitations when it comes to cultural coverage and competence, and in some cases show regional biases such as amplifying Western and Anglocentric viewpoints.…
Causal Effects of Trigger Words in Social Media Discussions: A Large-Scale Case Study about UK Politics on Reddit
Dimosthenis Antypas, Christian Arnold, Nedjma Ousidhoum +2
Political debates on social media often escalate quickly, leading to increased engagement as well as more emotional and polarised exchanges. Trigger points (Mau, Lux, and Westheuse…
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…