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cs.CL2025
MeTA-LoRA: Data-Efficient Multi-Task Fine-Tuning for Large Language Models
Bo Cheng, Xu Wang, Jinda Liu +2
Low-Rank Adaptation (LoRA) has emerged as one of the most widely used parameter-efficient fine-tuning (PEFT) methods for adapting large language models (LLMs) to downstream tasks.…
cs.CL2025
From Isolation to Alignment: Unified LoRA for Efficient Multi-Task Learning
Jinda Liu, Yi Chang, Yuan Wu
Parameter-Efficient Fine-Tuning (PEFT) is essential for adapting Large Language Models (LLMs) to multi-task scenarios. A prevailing trend in this field involves complex LoRA varian…
cs.LG2025
R-LoRA: Randomized Multi-Head LoRA for Efficient Multi-Task Learning
Jinda Liu, Yi Chang, Yuan Wu
Fine-tuning large language models (LLMs) is computationally expensive, and Low-Rank Adaptation (LoRA) provides a cost-effective solution by approximating weight updates through low…