Xingle Xu

Model merging dramatically reduces storage and computational resources by combining multiple expert models into a single multi-task model. However, existing methods struggle to maintain performance gains as the number of merged models increases. In this paper, we investigate the key obstacles tha...

P arameter- E fficient F ine- T uning ( PEFT ) has become a popular alternative to F ull-Parameter F ine- T uning ( FFT ), achieving similar performance on many benchmarks with far lower computational and memory costs. Yet, its effectiveness on complex tasks such as reasoning and instruction-foll...

Multimodal Sentiment Analysis aims to integrate information from various modalities to make complementary predictions. However, it often struggles with irrelevant or misleading visual and auditory information. Most existing approaches treat entire modality as an independent unit for feature enhan...

Multimodal Large Language Models (MLLMs) integrate visual encoders with Large Language Models (LLMs) and enable multimodal reasoning. However, for tasks that heavily rely on visual information, the model’s utilization of visual information remains unstable, which leads to reasoning failures. Prio...