An Empirical Study of Many-Shot In-Context Learning for Machine Translation of Low-Resource Languages
arXiv:2604.02596v3 Announce Type: replace Abstract: In-context learning (ICL) allows large language models (LLMs) to adapt to new tasks from a few examples, making it promising for languages underrepresented in pre-training. Recent work on many-shot ICL suggests that modern LLMs can further benefit from larger ICL examples enabled by their long context windows. However, such gains depend on careful example selection, and the inference cost can be prohibitive for low-resource language communities. In this paper, we present an empirical study of many-shot ICL for machine translation from English
The proliferation of long context window LLMs makes many-shot in-context learning feasible, while the need for equitable AI access in low-resource languages drives research into more efficient adaptation methods.
This research outlines a pathway to make advanced AI capabilities, specifically machine translation, more accessible and cost-effective for underserved linguistic communities, potentially reducing digital divides and fostering broader AI adoption.
The explicit focus on balancing performance gains with inference costs for low-resource languages signifies a growing industry recognition of these community-specific constraints in AI development and deployment.
- · Low-resource language communities
- · AI model developers
- · Global communication platforms
- · Linguistic diversity initiatives
- · Traditional translation services (long-term)
- · Monolingual content creators
Machine translation accuracy and accessibility improve significantly for languages previously lacking sufficient training data.
Increased availability of high-quality translation tools could foster greater cross-cultural understanding and economic exchange.
Nations with low-resource languages might gain greater digital sovereignty and participate more fully in the global digital economy.
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Read at arXiv cs.CL