Large language models (LLMs) are known to exhibit sycophancy by adjusting their responses toward users'expressed views. However, real users often justify their opinions through reasons, personal experiences, causal explanations, and broader narratives, raising the question of whether such narratives can further steer m...
Run Yang, Dan-Dan Zhu, Kai-Wei Zhang et al.· 0 citations
Multimodal Large Language Models (MLLMs) have demonstrated impressive capabilities in both visual understanding and code generation. However, existing benchmarks typically evaluate these two modalities in isolation, lacking a dedicated assessment of their unification, i.e., how a model can perceive complex visual struc...
Zi-Jian Chen, Zheng-Yu Chen, Bo-Han Liang et al.· 0 citations
This work introduces SciMIF, a novel benchmark designed to evaluate the capability of MLLMs in following complex scientific instructions, and proposes a comprehensive taxonomy comprising 10 constraint groups that captures both general functional requirements and discipline-specific characteristics.
Ye Shen, Yu-Ting Zheng, Dun Pei et al.· 0 citations
H2R-Bench provides a systematic diagnostic framework for evaluating whether video world models can bridge the human-to-robot embodiment gap and convert human manipulation observations into robot-centric training resources.
Ding-Yi Rong, Yue Shi, Chaofan Ma et al.· 1 citation
ELBench is introduced, the first benchmark to evaluate all four requirements (General Capability, Safety and Trustworthiness, Basic Education, and High-Level Cultivation) on the same models under a common protocol, combining curated public sources with newly synthesized safety and cultivation data.
Yi-Lin Jiang, Xiao-Rong Zhu, Fei Tan et al.· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
