8 citations · 10 across the 8 of their papers we have counts for
8 papers
PRISM-Bench: A Benchmark of Puzzle-Based Visual Tasks with CoT Error Detection
Yusu Qian, Cheng Wan, Chao Jia +3
Multimodal large language models (MLLMs) have achieved remarkable progress on vision-language tasks, yet their reasoning processes remain sometimes unreliable. We introduce PRISM-B…
Pico-Banana-400K: A Large-Scale Dataset for Text-Guided Image Editing
Yusu Qian, Eli Bocek-Rivele, Liangchen Song +5
Recent advances in multimodal models have demonstrated remarkable text-guided image editing capabilities, with systems like GPT-4o and Nano-Banana setting new benchmarks. However,…
GIE-Bench: Towards Grounded Evaluation for Text-Guided Image Editing
Yusu Qian, Jiasen Lu, Tsu-Jui Fu +5
Editing images using natural language instructions has become a natural and expressive way to modify visual content; yet, evaluating the performance of such models remains challeng…
UniVG: A Generalist Diffusion Model for Unified Image Generation and Editing
Tsu-Jui Fu, Yusu Qian, Chen Chen +3
Text-to-Image (T2I) diffusion models have shown impressive results in generating visually compelling images following user prompts. Building on this, various methods further fine-t…
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…
Reducing Gender Bias in Word-Level Language Models with a Gender-Equalizing Loss Function
Yusu Qian, Urwa Muaz, Ben Zhang +1
Gender bias exists in natural language datasets which neural language models tend to learn, resulting in biased text generation. In this research, we propose a debiasing approach b…