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Evaluating Stochastic Collapse and Implicit Bias in Multimodal Large Language Models
Huiyuan Zheng, Houtao Zhang, Boyang Wang +2
Current evaluations for Multimodal Large Language Models (MLLMs) overwhelmingly focus on utility-driven objectives, leaving model behavior under logic-neutral scenarios largely und…
Pet-Bench: Benchmarking the Abilities of Large Language Models as E-Pets in Social Network Services
Hongcheng Guo, Zheyong Xie, Shaosheng Cao +6
As interest in using Large Language Models for interactive and emotionally rich experiences grows, virtual pet companionship emerges as a novel yet underexplored application. Exist…
SNS-Bench-VL: Benchmarking Multimodal Large Language Models in Social Networking Services
Hongcheng Guo, Zheyong Xie, Shaosheng Cao +5
With the increasing integration of visual and textual content in Social Networking Services (SNS), evaluating the multimodal capabilities of Large Language Models (LLMs) is crucial…
H2HTalk: Evaluating Large Language Models as Emotional Companion
Boyang Wang, Yalun Wu, Hongcheng Guo +1
As digital emotional support needs grow, Large Language Model companions offer promising authentic, always-available empathy, though rigorous evaluation lags behind model advanceme…
Redefining Machine Translation on Social Network Services with Large Language Models
Hongcheng Guo, Fei Zhao, Shaosheng Cao +8
The globalization of social interactions has heightened the need for machine translation (MT) on Social Network Services (SNS), yet traditional models struggle with culturally nuan…
Cluster-Driven Expert Pruning for Mixture-of-Experts Large Language Models
Hongcheng Guo, Juntao Yao, Boyang Wang +5
Mixture-of-Experts (MoE) architectures have emerged as a promising paradigm for scaling large language models (LLMs) with sparse activation of task-specific experts. Despite their…