3 papers
cs.CL2025
Cheems: A Practical Guidance for Building and Evaluating Chinese Reward Models from Scratch
Xueru Wen, Jie Lou, Zichao Li +9
Reward models (RMs) are crucial for aligning large language models (LLMs) with human preferences. However, most RM research is centered on English and relies heavily on synthetic r…
cs.CV2025
Exploring Implicit Visual Misunderstandings in Multimodal Large Language Models through Attention Analysis
Pengfei Wang, Guohai Xu, Weinong Wang +3
Recent advancements have enhanced the capability of Multimodal Large Language Models (MLLMs) to comprehend multi-image information. However, existing benchmarks primarily evaluate…
cs.CV2025
MLLM-Selector: Necessity and Diversity-driven High-Value Data Selection for Enhanced Visual Instruction Tuning
Yiwei Ma, Guohai Xu, Xiaoshuai Sun +4
Visual instruction tuning (VIT) has emerged as a crucial technique for enabling multi-modal large language models (MLLMs) to follow user instructions adeptly. Yet, a significant ga…