System prompts are instructions configured by developers to govern the behaviors of foundation models in AI applications. They are used throughout commercial AI products, but are rarely disclosed to the public or regulators, creating a serious trust and accountability gap in the wide deployment of AI systems. In this p...
Xiangning Lin, Shenzhe Zhu, Shu Yang et al.· arXiv.org· 0 citations
This work introduces Audio-Zero, the first label-free self-evolution framework in the field of LALMs that improves fine-grained auditory perception and reasoning and reveals that increasingly fine-grained auditory descriptions emerge naturally from game pressure.
Siqian Tong, Xuan Li, Chao-Zhuo Li et al.· arXiv.org· 1 citation
This work introduces Polarity-Prompt Contrastive Decoding (PopCD), a test-time behavior control method that generalizes contrastive decoding to broader enhancement settings and is applicable to both LLMs and Vision-Language Models without additional training.
Bao-Long Bi, Yuyao Ge, Shenghua Liu et al.· IEEE Transactions on Pattern...· 0 citations
LP-SFT, a Local-Preserving Supervised Fine-Tuning objective designed to explicitly protect this inherent entropy structure, improves overall performance over vanilla SFT and recent SFT-enhancement baselines, suggesting that local preservation helps mitigate capability degradation without collapsing sampling-accessible...
Yueyang Wang, Baolong Bi, Shuo Lu et al.· 0 citations
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