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
SafetyFlow is introduced, the first agent-flow system designed to automate the construction of LLM safety benchmarks, and includes the first fully automated benchmarking pipeline and a comprehensive safety benchmark.
Xiang-Yang Zhu, Yuan Tian, Chunyi Li et al.· arXiv.org· 3 citations
This paper introduces SafeAtlas-VL, a dataset of 1.5M training instances that places image-, request-, and response-level judgments on a five-level ordered scale, and trains the SafeAtlas Guard series of models via target-conditioned tuning for multimodal safety detection.
Zong-Rui Wang, Xiang-Yang Zhu, Sixiang Wang et al.· 0 citations
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