11 papers
CustomerSim: Benchmarking and Aligning Multimodal Language Models as Retail User Simulators
Yada Pruksachatkun, Yixin Wan, Elaine Wan +4
We present CustomerSim, an environment and benchmark to evaluate the extent to which Multimodal Large Language Models (MLLMs) can simulate realistic, persona-driven customer behavi…
VisRet: Visualization Improves Knowledge-Intensive Text-to-Image Retrieval
Di Wu, Yixin Wan, Kai-Wei Chang
Text-to-image retrieval (T2I retrieval) remains challenging because cross-modal embeddings often behave as bags of concepts, underrepresenting structured visual relationships such…
MTMCS-Bench: Evaluating Contextual Safety of Multimodal Large Language Models in Multi-Turn Dialogues
Zheyuan Liu, Dongwhi Kim, Yixin Wan +4
Multimodal large language models (MLLMs) are increasingly deployed as assistants that interact through text and images, making it crucial to evaluate contextual safety when risk de…
MotionEdit: Benchmarking and Learning Motion-Centric Image Editing
Yixin Wan, Lei Ke, Wenhao Yu +2
We introduce MotionEdit, a novel dataset for motion-centric image editing-the task of modifying subject actions and interactions while preserving identity, structure, and physical…
Where Fact Ends and Fairness Begins: Redefining AI Bias Evaluation through Cognitive Biases
Jen-tse Huang, Yuhang Yan, Linqi Liu +4
Recent failures such as Google Gemini generating people of color in Nazi-era uniforms illustrate how AI outputs can be factually plausible yet socially harmful. AI models are incre…
The Male CEO and the Female Assistant: Evaluation and Mitigation of Gender Biases in Text-To-Image Generation of Dual Subjects
Yixin Wan, Kai-Wei Chang
Recent large-scale T2I models like DALLE-3 have made progress in reducing gender stereotypes when generating single-person images. However, significant biases remain when generatin…