Summary of Overview Of the First Workshop on Language Models For Low-resource Languages (loreslm 2025), by Hansi Hettiarachchi et al.
Overview of the First Workshop on Language Models for Low-Resource Languages (LoResLM 2025)
by Hansi Hettiarachchi, Tharindu Ranasinghe, Paul Rayson, Ruslan Mitkov, Mohamed Gaber, Damith Premasiri, Fiona Anting Tan, Lasitha Uyangodage
First submitted to arxiv on: 20 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The LoResLM 2025 workshop brought together researchers to discuss advancements in neural language models tailored to low-resource languages. The event aimed to mitigate linguistic biases towards high-resource languages, fostering inclusive natural language processing (NLP) practices. With 35 accepted papers from 52 submissions, the workshop showcased a diverse range of contributions covering eight language families and 13 research areas. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a workshop that helps fix problems with language models when they’re used for languages that don’t have a lot of data. The goal is to make sure language models are fair and can be used for all languages, not just the popular ones. The workshop had many submissions and accepted papers, which means there’s lots of new research in this area. |
Keywords
» Artificial intelligence » Natural language processing » Nlp
