36 citations · 44 across the 11 of their papers we have counts for
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cs.LG2025
Optimizing Mixture of Block Attention
Guangxuan Xiao, Junxian Guo, Kasra Mazaheri +1
Mixture of Block Attention (MoBA) (Lu et al., 2025) is a promising building block for efficiently processing long contexts in LLMs by enabling queries to sparsely attend to a small…
cs.LG2025
Twilight: Adaptive Attention Sparsity with Hierarchical Top- Pruning
Chaofan Lin, Jiaming Tang, Shuo Yang +6
Leveraging attention sparsity to accelerate long-context large language models (LLMs) has been a hot research topic. However, current algorithms such as sparse attention or key-val…
cs.LG2024
BitDelta: Your Fine-Tune May Only Be Worth One Bit
James Liu, Guangxuan Xiao, Kai Li +4
Large Language Models (LLMs) are typically trained in two phases: pre-training on large internet-scale datasets, and fine-tuning for downstream tasks. Given the higher computationa…