5 papers
CD-LoRA: Consistency-Driven Low-Rank Adaptation for Multi-Task Fine-Tuning
Qian Zha, Jinda Liu, Yuan Wu +1
While Multi-Task Learning (MTL) is essential for adapting Large Language Models (LLMs) to diverse domains, prevailing LoRA-based methods rely on complex routing mechanisms that par…
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.…
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…
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…
Cyclic Vision-Language Manipulator: Towards Reliable and Fine-Grained Image Interpretation for Automated Report Generation
Yingying Fang, Zihao Jin, Shaojie Guo +7
Despite significant advancements in automated report generation, the opaqueness of text interpretability continues to cast doubt on the reliability of the content produced. This pa…