8 papers
FLY-EVAL++: An Evidence-Driven Evaluation Protocol for Safety-Constrained Flight Prediction with Large Language Models
Yalun Wu, Junfeng Fang, Jiawei Wang +6
Evaluating large language models (LLMs) in safety-critical, physics-governed environments requires more than accuracy-based metrics, because predictions that are numerically close…
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…
DinoCompanion: An Attachment-Theory Informed Multimodal Robot for Emotionally Responsive Child-AI Interaction
Boyang Wang, Yuhao Song, Jinyuan Cao +3
Children's emotional development fundamentally relies on secure attachment relationships, yet current AI companions lack the theoretical foundation to provide developmentally appro…
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…
U2-BENCH: Benchmarking Large Vision-Language Models on Ultrasound Understanding
Anjie Le, Henan Liu, Yue Wang +18
Ultrasound is a widely-used imaging modality critical to global healthcare, yet its interpretation remains challenging due to its varying image quality on operators, noises, and an…
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…
DependEval: Benchmarking LLMs for Repository Dependency Understanding
Junjia Du, Yadi Liu, Hongcheng Guo +4
While large language models (LLMs) have shown considerable promise in code generation, real-world software development demands advanced repository-level reasoning. This includes un…