7 citations · 21 across the 34 of their papers we have counts for
12 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.…
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…
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…
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…
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…
LoRA-MGPO: Mitigating Double Descent in Low-Rank Adaptation via Momentum-Guided Perturbation Optimization
Yupeng Chang, Chenlu Guo, Yi Chang +1
Parameter-efficient fine-tuning (PEFT), particularly Low-Rank Adaptation (LoRA), adapts large language models (LLMs) by training only a small fraction of parameters. However, as th…
A Survey on Data Contamination for Large Language Models
Yuxing Cheng, Yi Chang, Yuan Wu
Recent advancements in Large Language Models (LLMs) have demonstrated significant progress in various areas, such as text generation and code synthesis. However, the reliability of…
SegRet: An Efficient Design for Semantic Segmentation with Retentive Network
Zhiyuan Li, Yi Chang, Yuan Wu
With the rapid evolution of autonomous driving technology and intelligent transportation systems, semantic segmentation has become increasingly critical. Precise interpretation and…
A Survey of RWKV
Zhiyuan Li, Tingyu Xia, Yi Chang +1
The Receptance Weighted Key Value (RWKV) model offers a novel alternative to the Transformer architecture, merging the benefits of recurrent and attention-based systems. Unlike con…