Self-attention gives LLMs fine-grained, query-dependent access to context, but dense token interactions incur quadratic prefill cost and a key--value cache growing with context length. Research thus spans explicit-memory compression, sparse access, recurrent state construction, structured state dynamics, and heterogene...
Zhen-Tao Tan, Jing-Yi Shen, Yan-Bo Li et al.· 0 citations
Multimodal Large Language Models face significant efficiency challenges that stem from two distinct yet coupled sources: data redundancy and computational redundancy. While most methods focus on data redundancy by pruning visual tokens from the output of the visual encoder or computing redundancy in LLM decoders using...
Tian-Xiang Chen, Zhentao Tan, Zi Ye et al.· IEEE Transactions on Pattern...· 0 citations
NSFT (Neural Sub-expert Fine-Tuning), a fine-grained PEFT framework that refines MoE adaptation from experts to sub-experts, is proposed and suggests that sub-expert-level adaptation is a more precise and efficient PEFT paradigm for MoE LLMs.
Zhen-Tao Tan, Chang Liu, Yao Liu et al.· 1 citation
This paper introduces Caduceus, a family of MoE-enhanced foundation models built with a hierarchical pre-training paradigm to jointly integrate biological and natural language, and incorporates a multi-task instruction tuning phase, enabling robust protein parsing and natural language question answering.
Ming-Ze Yin, Yiheng Zhu, Jialu Wu et al.· Proceedings of the 32nd ACM...· 1 citation
This paper introduces Caduceus, a family of MoE-enhanced foundation models built with a hierarchical pre-training paradigm to jointly integrate biological and natural language, and incorporates a multi-task instruction tuning phase, enabling robust protein parsing and natural language question answering.
Mingze Yin, Yiheng Zhu, Jialu Wu et al.· Proceedings of the 32nd ACM...· 0 citations
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