Overview of the First Workshop on Language Models for Low-Resource Languages (LoResLM 2025)
This workshop promotes linguistic inclusivity in NLP by focusing on low-resource languages, but it is incremental as it builds on existing efforts to address biases in language models.
The paper describes the first Workshop on Language Models for Low-Resource Languages (LoResLM 2025), which aimed to address the problem of linguistic biases in neural language models towards high-resource languages by providing a forum for researchers to share work, resulting in 35 accepted papers from 52 submissions covering diverse low-resource languages and research areas.
The first Workshop on Language Models for Low-Resource Languages (LoResLM 2025) was held in conjunction with the 31st International Conference on Computational Linguistics (COLING 2025) in Abu Dhabi, United Arab Emirates. 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, following the recent advancements in neural language models and their linguistic biases towards high-resource languages. LoResLM 2025 attracted notable interest from the natural language processing (NLP) community, resulting in 35 accepted papers from 52 submissions. These contributions cover a broad range of low-resource languages from eight language families and 13 diverse research areas, paving the way for future possibilities and promoting linguistic inclusivity in NLP.