Overview of the First Workshop on Language Models for Low-Resource Languages (LoResLM 2025)

• URL: http://arxiv.org/abs/2412.16365v1
• Date: Fri, 20 Dec 2024 21:55:32 GMT
• Title: Overview of the First Workshop on Language Models for Low-Resource Languages (LoResLM 2025)
• Authors: Hansi Hettiarachchi, Tharindu Ranasinghe, Paul Rayson, Ruslan Mitkov, Mohamed Gaber, Damith Premasiri, Fiona Anting Tan, Lasitha Uyangodage,
• Abstract summary: LoResLM 2025 was held in conjunction with the 31st International Conference on Computational Linguistics (COLING 2025) in Abu Dhabi, United Arab Emirates. LoResLM 2025 attracted notable interest from the natural language processing (NLP) community, resulting in 35 accepted papers from 52 submissions.
• Score: 8.529133508189737
• License:
• Abstract: 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.

Related papers

• Are Multilingual Language Models an Off-ramp for Under-resourced Languages? Will we arrive at Digital Language Equality in Europe in 2030? [2.1471774065088036]
  Large language models (LLMs) demonstrate unprecedented capabilities and define the state of the art for almost all natural language processing (NLP) tasks. LLMs can only be trained for languages for which a sufficient amount of pre-training data is available. This paper examines the current situation in terms of technology support and summarises related work.
  arXiv  Detail & Related papers  (2025-02-18T14:20:27Z)
• Foundation Models for Low-Resource Language Education (Vision Paper) [31.80093028879394]
  Large language models (LLMs) are powerful tools for working with natural language. LLMs face challenges when applied to low-resource languages due to limited training data and difficulty in understanding cultural nuances. This paper discusses how LLMs could enhance education for low-resource languages, emphasizing practical applications and benefits.
  arXiv  Detail & Related papers  (2024-12-06T04:34:45Z)
• Multilingual Large Language Model: A Survey of Resources, Taxonomy and Frontiers [81.47046536073682]
  We present a review and provide a unified perspective to summarize the recent progress as well as emerging trends in multilingual large language models (MLLMs) literature. We hope our work can provide the community with quick access and spur breakthrough research in MLLMs.
  arXiv  Detail & Related papers  (2024-04-07T11:52:44Z)
• ArabicMMLU: Assessing Massive Multitask Language Understanding in Arabic [51.922112625469836]
  We present datasetname, the first multi-task language understanding benchmark for the Arabic language. Our data comprises 40 tasks and 14,575 multiple-choice questions in Modern Standard Arabic (MSA) and is carefully constructed by collaborating with native speakers in the region. Our evaluations of 35 models reveal substantial room for improvement, particularly among the best open-source models.
  arXiv  Detail & Related papers  (2024-02-20T09:07:41Z)
• Multilingual Word Embeddings for Low-Resource Languages using Anchors and a Chain of Related Languages [54.832599498774464]
  We propose to build multilingual word embeddings (MWEs) via a novel language chain-based approach. We build MWEs one language at a time by starting from the resource rich source and sequentially adding each language in the chain till we reach the target. We evaluate our method on bilingual lexicon induction for 4 language families, involving 4 very low-resource (5M tokens) and 4 moderately low-resource (50M) target languages.
  arXiv  Detail & Related papers  (2023-11-21T09:59:29Z)
• Findings of the 2023 ML-SUPERB Challenge: Pre-Training and Evaluation over More Languages and Beyond [89.54151859266202]
  The 2023 Multilingual Speech Universal Performance Benchmark (ML-SUPERB) Challenge expands upon the acclaimed SUPERB framework. The challenge garnered 12 model submissions and 54 language corpora, resulting in a comprehensive benchmark encompassing 154 languages. The findings indicate that merely scaling models is not the definitive solution for multilingual speech tasks.
  arXiv  Detail & Related papers  (2023-10-09T08:30:01Z)
• BenLLMEval: A Comprehensive Evaluation into the Potentials and Pitfalls of Large Language Models on Bengali NLP [17.362068473064717]
  Large Language Models (LLMs) have emerged as one of the most important breakthroughs in NLP. This paper introduces BenLLM-Eval, which consists of a comprehensive evaluation of LLMs to benchmark their performance in the Bengali language. Our experimental results demonstrate that while in some Bengali NLP tasks, zero-shot LLMs could achieve performance on par, or even better than current SOTA fine-tuned models.
  arXiv  Detail & Related papers  (2023-09-22T20:29:34Z)
• A Survey of Corpora for Germanic Low-Resource Languages and Dialects [18.210880703295253]
  This work focuses on low-resource languages and in particular non-standardized low-resource languages. We make our overview of over 80 corpora publicly available to facilitate research.
  arXiv  Detail & Related papers  (2023-04-19T16:45:16Z)
• Including Signed Languages in Natural Language Processing [48.62744923724317]
  Signed languages are the primary means of communication for many deaf and hard of hearing individuals. This position paper calls on the NLP community to include signed languages as a research area with high social and scientific impact.
  arXiv  Detail & Related papers  (2021-05-11T17:37:55Z)
• Learning to Learn Morphological Inflection for Resource-Poor Languages [105.11499402984482]
  We propose to cast the task of morphological inflection - mapping a lemma to an indicated inflected form - for resource-poor languages as a meta-learning problem. Treating each language as a separate task, we use data from high-resource source languages to learn a set of model parameters. Experiments with two model architectures on 29 target languages from 3 families show that our suggested approach outperforms all baselines.
  arXiv  Detail & Related papers  (2020-04-28T05:13:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.