3 papers
cs.LG2026
Empowering Reliable Visual-Centric Instruction Following in MLLMs
Weilei He, Feng Ju, Zhiyuan Fan +3
Evaluating the instruction-following (IF) capabilities of Multimodal Large Language Models (MLLMs) is essential for rigorously assessing how faithfully model outputs adhere to user…
cs.CL2025
MMBoundary: Advancing MLLM Knowledge Boundary Awareness through Reasoning Step Confidence Calibration
Zhitao He, Sandeep Polisetty, Zhiyuan Fan +3
In recent years, multimodal large language models (MLLMs) have made significant progress but continue to face inherent challenges in multimodal reasoning, which requires multi-leve…
cs.CL2023
Enhanced Chart Understanding in Vision and Language Task via Cross-modal Pre-training on Plot Table Pairs
Mingyang Zhou, Yi R. Fung, Long Chen +3
Building cross-model intelligence that can understand charts and communicate the salient information hidden behind them is an appealing challenge in the vision and language(V+L) co…