Effective collaboration requires language models to understand what others know and believe, and to act accordingly. Models that assume others share their information may omit necessary communication and undermine coordination. Many theory-of-mind (ToM) benchmarks assess mental-state inference through story questions,...
Zhen-Shuai Yin, Yong-Xin Guo, Ming-Da Zhang et al.· 0 citations
Knowing when they are reliable is essential for language models to support human-AI collaboration. Yet interaction feedback is not always complete or correct, and may mislead models'understanding of their own capabilities. We introduce LADDER to study how language models assess themselves under unreliable feedback. Our...
Zhen-Shuai Yin, Jian-Shuai Liang, Xu-Yue Xiong et al.· 0 citations
Existing jailbreaks against aligned LLMs are discrete artifacts whose surface forms are easy to fingerprint and patch. We argue that the broader failure mode may lie not in any specific prompt, but in natural writing registers that safety tuning under-covers. Building on this insight, we introduce VAR, the first jailbr...
Zhongze Luo, Rui-He Shi, Zhenshuai Yin et al.· 0 citations
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