The second workshop on Language Models for Low-Resource Languages (LoResLM 2026) was held in conjunction with the 19th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2026) in Rabat, Morocco. This workshop mainly aimed to provide a forum for researchers to share and discuss their ongoing work on language models (LMs) focusing on low-resource languages and dialects, following recent advancements in neural language models and their linguistic biases towards high- resource languages. LoResLM 2026 attracted a notable interest from the natural language processing (NLP) community, resulting in 55 accepted papers from 79 submissions. These contributions cover a broad range of low-resource languages from 13 language families and 11 diverse research areas, paving the way for future possibilities and promoting linguistic inclusivity in NLP.

Automatic socio-political and crisis event detection has been a challenge for natural language processing as well as social and political science communities, due to the diversity and nuance in such events and high accuracy requirements. In this paper, we propose an approach which can handle both document and cross-sentence level event detection in a multilingual setting using pretrained transformer models. Our approach became the winning solution in document level predictions and secured the 3rd place in cross-sentence level predictions for the English language. We could also achieve competitive results for other languages to prove the effectiveness and universality of our approach.