Object hallucination remains a major obstacle for large vision-language models (LVLMs) to generate reliable content. An intuitive mitigation strategy is to suppress hallucination-related components in hidden representations. However, these components may also contain useful information, and suppressing them can weaken...
Chen Zhao, Xing-Ping Dong, Jia-Chun Shi et al.· 0 citations
UniPrefill is proposed, a prefill acceleration framework applicable to virtually any model architecture, which directly accelerates the model's computation at the token level and achieves up to 2.1x speedup in Time-To-First-Token (TTFT), with the acceleration becoming increasingly pronounced as the number of concurrent...
This paper introduces a mean correction term that effectively suppresses the approximation error, keeping performance degradation manageable even at extreme sparsity levels, and redesigns the sparse attention operator with PackGQA memory access, warp specialization, and pingpong pipelining.
Qihang Fan, Huai-Bo Huang, Zhiying Wu et al.· 2 citations
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