RefLoRA picks a per-step optimal low-rank factorization (a matrix geometric mean) that balances LoRA's factors, improving fine-tuning convergence and accuracy.
SemEval-2017 task 1: Semantic textual similarity-multilingual and cross-lingual focused evaluation
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RefLoRA: Refactored Low-Rank Adaptation for Efficient Fine-Tuning of Large Models
RefLoRA picks a per-step optimal low-rank factorization (a matrix geometric mean) that balances LoRA's factors, improving fine-tuning convergence and accuracy.