LatentLLM: Attention-Aware Joint Tensor Compression

• URL: http://arxiv.org/abs/2505.18413v1
• Date: Fri, 23 May 2025 22:39:54 GMT
• Title: LatentLLM: Attention-Aware Joint Tensor Compression
• Authors: Toshiaki Koike-Akino, Xiangyu Chen, Jing Liu, Ye Wang, Pu, Wang, Matthew Brand,
• Abstract summary: Large language models (LLMs) and large multi-modal models (LMMs) require a massive amount of computational and memory resources.<n>We propose a new framework to convert such LLMs/LMMs into a reduced-dimension latent structure.
• Score: 50.33925662486034
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Modern foundation models such as large language models (LLMs) and large multi-modal models (LMMs) require a massive amount of computational and memory resources. We propose a new framework to convert such LLMs/LMMs into a reduced-dimension latent structure. Our method extends a local activation-aware tensor decomposition to a global attention-aware joint tensor de-composition. Our framework can significantly improve the model accuracy over the existing model compression methods when reducing the latent dimension to realize computationally/memory-efficient LLMs/LLMs. We show the benefit on several benchmark including multi-modal reasoning tasks.

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