5 citations · 9 across the 20 of their papers we have counts for
8 papers · 1 filter
Training-Free Hashing-Based Attention via Binary Principal Components
Daohai Yu, Zhanpeng Zeng, Keyu Chen +6
Long-context large language models (LLMs) are increasingly deployed in real-world applications, yet self-attention remains a major efficiency bottleneck -- especially during decodi…
Efficiently Aligning Draft Models via Parameter- and Data-Efficient Adaptation
Luxi Lin, Zhihang Lin, Zhanpeng Zeng +5
Speculative decoding accelerates LLM inference but suffers from performance degradation when target models are fine-tuned for specific domains. A naive solution is to retrain draft…
Towards Efficient Large Language Reasoning Models via Extreme-Ratio Chain-of-Thought Compression
Yuntian Tang, Bohan Jia, Wenxuan Huang +7
Chain-of-Thought (CoT) reasoning successfully enhances the reasoning capabilities of Large Language Models (LLMs), yet it incurs substantial computational overhead for inference. E…
Training Long-Context LLMs Efficiently via Chunk-wise Optimization
Wenhao Li, Yuxin Zhang, Gen Luo +2
While long-context large language models (LLMs) exhibit remarkable document processing capabilities, their prohibitively high training costs often hinder customized applications. T…
Dynamic Low-Rank Sparse Adaptation for Large Language Models
Weizhong Huang, Yuxin Zhang, Xiawu Zheng +4
Despite the efficacy of network sparsity in alleviating the deployment strain of Large Language Models (LLMs), it endures significant performance degradation. Applying Low-Rank Ada…
Determining Layer-wise Sparsity for Large Language Models Through a Theoretical Perspective
Weizhong Huang, Yuxin Zhang, Xiawu Zheng +2
In this paper, we address the challenge of determining the layer-wise sparsity rates of large language models (LLMs) through a theoretical perspective. Specifically, we identify a…
Towards Efficient Automatic Self-Pruning of Large Language Models
Weizhong Huang, Yuxin Zhang, Xiawu Zheng +2
Despite exceptional capabilities, Large Language Models (LLMs) still face deployment challenges due to their enormous size. Post-training structured pruning is a promising solution…
Boosting the Cross-Architecture Generalization of Dataset Distillation through an Empirical Study
Lirui Zhao, Yuxin Zhang, Fei Chao +1
The poor cross-architecture generalization of dataset distillation greatly weakens its practical significance. This paper attempts to mitigate this issue through an empirical study…