3 papers
cs.CV2026
Fast-Slow Efficient Training for Multimodal Large Language Models via Visual Token Pruning
Dingkun Zhang, Shuhan Qi, Yulin Wu +3
Multimodal Large Language Models (MLLMs) suffer from severe training inefficiency issue, which is associated with their massive model sizes and visual token numbers. Existing effor…
cs.CL2025
MDIT-Bench: Evaluating the Dual-Implicit Toxicity in Large Multimodal Models
Bohan Jin, Shuhan Qi, Kehai Chen +2
The widespread use of Large Multimodal Models (LMMs) has raised concerns about model toxicity. However, current research mainly focuses on explicit toxicity, with less attention to…
cs.LG2025
Merge then Realign: Simple and Effective Modality-Incremental Continual Learning for Multimodal LLMs
Dingkun Zhang, Shuhan Qi, Xinyu Xiao +2
Recent advances in Multimodal Large Language Models (MLLMs) have enhanced their versatility as they integrate a growing number of modalities. Considering the heavy cost of training…