RL with chrF reward trains LLMs to better utilize in-context linguistic knowledge for zero-shot translation of unseen languages, outperforming ICL and SFT.
Hire a Linguist!: Learning Endangered Languages in LLM s with In-Context Linguistic Descriptions
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.CL 3years
2026 3verdicts
UNVERDICTED 3representative citing papers
Decomposing annotation tasks using centers from centering theory reduces aggregate inferential load via a degrees-of-freedom model and enables better sub-task allocation.
Lius improves LLM translation for Kupang Malay by 4-13 points over baselines via continual instruction tuning with dictionary-derived instructions.
citing papers explorer
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Reinforcement Learning Elicits Contextual Learning of Unseen Language Translation
RL with chrF reward trains LLMs to better utilize in-context linguistic knowledge for zero-shot translation of unseen languages, outperforming ICL and SFT.
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Task Decomposition for Efficient Annotation
Decomposing annotation tasks using centers from centering theory reduces aggregate inferential load via a degrees-of-freedom model and enables better sub-task allocation.
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Lius: Translation Model Based Instructional Lingustic Using Continual Instruction Tuning In Kupang Malay
Lius improves LLM translation for Kupang Malay by 4-13 points over baselines via continual instruction tuning with dictionary-derived instructions.