7 citations · 9 across the 24 of their papers we have counts for
13 papers · 1 filter
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.…
A Survey of Retentive Network
Haiqi Yang, Zhiyuan Li, Yi Chang +1
Retentive Network (RetNet) represents a significant advancement in neural network architecture, offering an efficient alternative to the Transformer. While Transformers rely on sel…
THINK-Bench: Evaluating Thinking Efficiency and Chain-of-Thought Quality of Large Reasoning Models
Zhiyuan Li, Yi Chang, Yuan Wu
Large reasoning models (LRMs) have achieved impressive performance in complex tasks, often outperforming conventional large language models (LLMs). However, the prevalent issue of…
NLoRA: Nyström-Initiated Low-Rank Adaptation for Large Language Models
Chenlu Guo, Yuan Wu, Yi Chang
Parameter-efficient fine-tuning (PEFT) is essential for adapting large language models (LLMs), with low-rank adaptation (LoRA) being the most popular approach. However, LoRA suffer…
Transfer-Prompting: Enhancing Cross-Task Adaptation in Large Language Models via Dual-Stage Prompts Optimization
Yupeng Chang, Yi Chang, Yuan Wu
Large language models (LLMs) face significant challenges when balancing multiple high-level objectives, such as generating coherent, relevant, and high-quality responses while main…
Mixup Model Merge: Enhancing Model Merging Performance through Randomized Linear Interpolation
Yue Zhou, Yi Chang, Yuan Wu
Model merging aims to integrate multiple task-specific models into a unified model that inherits the capabilities of the task-specific models, without additional training. Existing…