A prompt-and-alignment fine-tuning recipe is claimed to beat multilingual baselines on MLQA, XQuAD, and PAWS-X under low-resource data, but lacks reproducible experimental detail.
Towards low-resource languages machine translation: A language-specific fine-tuning with LoRA for specialized large language models
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Transferable Modeling Strategies for Low-Resource LLM Tasks: A Prompt and Alignment-Based Approach
A prompt-and-alignment fine-tuning recipe is claimed to beat multilingual baselines on MLQA, XQuAD, and PAWS-X under low-resource data, but lacks reproducible experimental detail.