3 papers
cs.LG2025
Why Do More Experts Fail? A Theoretical Analysis of Model Merging
Zijing Wang, Xingle Xu, Yongkang Liu +6
Model merging dramatically reduces storage and computational resources by combining multiple expert models into a single multi-task model. Although recent model merging methods hav…
cs.CL2025
SSMLoRA: Enhancing Low-Rank Adaptation with State Space Model
Jiayang Yu, Yihang Zhang, Bin Wang +3
Fine-tuning is a key approach for adapting language models to specific downstream tasks, but updating all model parameters becomes impractical as model sizes increase. Parameter-Ef…
cs.SE2025
SolEval: Benchmarking Large Language Models for Repository-level Solidity Code Generation
Zhiyuan Peng, Xin Yin, Rui Qian +5
Large language models (LLMs) have transformed code generation. However, most existing approaches focus on mainstream languages such as Python and Java, neglecting the Solidity lang…