4 papers
ACE-Merging: Data-Free Model Merging with Adaptive Covariance Estimation
Bo Xu, Haotian Wu, Hehai Lin +4
Model merging aims to combine multiple task-specific expert models into a single model while preserving generalization across diverse tasks. However, interference among experts, es…
Unveiling Modality Bias: Automated Sample-Specific Analysis for Multimodal Misinformation Benchmarks
Hehai Lin, Hui Liu, Shilei Cao +3
Numerous multimodal misinformation benchmarks exhibit bias toward specific modalities, allowing detectors to make predictions based solely on one modality. While previous research…
FURINA: A Fully Customizable Role-Playing Benchmark via Scalable Multi-Agent Collaboration Pipeline
Haotian Wu, Shufan Jiang, Chios Chen +5
As large language models (LLMs) advance in role-playing (RP) tasks, existing benchmarks quickly become obsolete due to their narrow scope, outdated interaction paradigms, and limit…
Self-Correction is More than Refinement: A Learning Framework for Visual and Language Reasoning Tasks
Jiayi He, Hehai Lin, Qingyun Wang +2
While Vision-Language Models (VLMs) have shown remarkable abilities in visual and language reasoning tasks, they invariably generate flawed responses. Self-correction that instruct…