3 papers indexed
Zixin Chen, Haotian Li, Ziang Xiao, Huamin Qu, Xing Xie
As LLMs become increasingly capable of completing tasks for users, a central concern is that everyday AI use may become primarily cognitive offloading, eroding the opportunities through which people develop their own capabilities. We analyse large-scale human--LLM conversations t…
Han Jiang, Sunbeom Kwon, Jinwen Luo, Ziang Xiao, Susu Zhang
AI benchmarks increasingly leverage item-level statistical models, particularly item response theory (IRT), to estimate model capabilities, rank systems, select informative examples, and diagnose benchmark quality. However, AI benchmark data often departs from the data regime of…
Yijiang Li, Huiqi Zou, Bingyang Wang, Ziang Xiao
Multi-modal Large Language Models (MLLMs) have made substantial advances on benchmarks, yet their real-world effectiveness remains uncertain. This gap stems from the fundamental misalignment between benchmarks in controlled, static settings and the dynamic, interactive, and conte…