8 citations · 10 across the 8 of their papers we have counts for
3 papers · 1 filter
Understanding Alignment in Multimodal LLMs: A Comprehensive Study
Elmira Amirloo, Jean-Philippe Fauconnier, Christoph Roesmann +8
Preference alignment has become a crucial component in enhancing the performance of Large Language Models (LLMs), yet its impact in Multimodal Large Language Models (MLLMs) remains…
MIA-Bench: Towards Better Instruction Following Evaluation of Multimodal LLMs
Yusu Qian, Hanrong Ye, Jean-Philippe Fauconnier +3
We introduce MIA-Bench, a new benchmark designed to evaluate multimodal large language models (MLLMs) on their ability to strictly adhere to complex instructions. Our benchmark com…
How Easy is It to Fool Your Multimodal LLMs? An Empirical Analysis on Deceptive Prompts
Yusu Qian, Haotian Zhang, Yinfei Yang +1
The remarkable advancements in Multimodal Large Language Models (MLLMs) have not rendered them immune to challenges, particularly in the context of handling deceptive information i…