mlsanityData to 7 Oct 2026

Foundation Models for Low-Resource Language Education (Vision Paper)

arXiv 2412.04774 ↗First posted 6 Dec 2024cs.CLcited by 5

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Abstract (arXiv)

Recent studies show that large language models (LLMs) are powerful tools for working with natural language, bringing advances in many areas of computational linguistics. However, these models face challenges when applied to low-resource languages due to limited training data and difficulty in understanding cultural nuances. Research is now focusing on multilingual models to improve LLM performance for these languages. Education in these languages also struggles with a lack of resources and qualified teachers, particularly in underdeveloped regions. Here, LLMs can be transformative, supporting innovative methods like community-driven learning and digital platforms. This paper discusses how LLMs could enhance education for low-resource languages, emphasizing practical applications and benefits.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 37 of its 62 references that are in the map. First posted on arXiv 6 Dec 2024.

Builds on

37 of 62 references.

References · 37

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25 more
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 3.

5 papers.

  1. 2025Large Language Models for Bioinformaticscited by 3
  2. 2025Achieving Trustworthy Real-Time Decision Support Systems with Low-Latency Interpretable AI Modelscited by 1
  3. 2026RoMathExam: A Longitudinal Dataset of Romanian Math Exams (1895-2025) with a Seven-Decade Core (1957-2025)cited by 0
  4. 2026XLGoBench: Detecting cross-lingual skill gaps with algorithmic taskscited by 0
  5. 2026From Lexicon to AI: A Structured-Data Pipeline for Specialized Conversational Systems in Low-Resource Languagescited by 0