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20222026
most citedImproving the Adversarial Robustness of NLP Models by Information Bottleneck

21 citations · 64 across the 23 of their papers we have counts for

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2 papers · 1 filter

cs.CL2026

CustomerSim: Benchmarking and Aligning Multimodal Language Models as Retail User Simulators

Yada Pruksachatkun, Yixin Wan, Xingrun Chen +2

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…

cs.CL2026

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…