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This is a Systematic Review study.

September 30, 2025Open Access

Overcoming data scarcity in generative language modelling for low-resource languages: a systematic review

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Data scarcity is a significant barrier for low-resource languages, impacting accessibility and representation in NLP.
54 studies reveal technical approaches, like data augmentation and multilingual training, crucial for improving generative models.
Transformer-based models dominate current architecture choices, emphasizing the need for broader representation among low-resource languages.
Evaluation methods vary widely, suggesting a lack of standardization that could limit advancements in equitable language technologies.

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Implication

Systematic review identifies strategies for data scarcity in low-resource languages, highlighting transformer-based models and evaluation inconsistencies.

Cite This Study

McGiff et al. (2025) studied this question.

synapsesocial.com/papers/68dc1e358a7d58c25ebb19c3 https://doi.org/10.21203/rs.3.rs-7075948/v1

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Implication

Systematic review identifies strategies for data scarcity in low-resource languages, highlighting transformer-based models and evaluation inconsistencies.

Overcoming data scarcity in generative language modelling for low-resource languages: a systematic review

Key Points

Discussion

Implication

Cite This Study

Overcoming data scarcity in generative language modelling for low-resource languages: a systematic review

Key Points

Discussion

Implication

Cite This Study

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