activity
20202026
most citedcosFormer: Rethinking Softmax in Attention

65 citations · 80 across the 12 of their papers we have counts for

collaborators
Showing cs.CLShow all

10 papers · 1 filter

cs.CL2026

Hybrid Gated Attention

Zekun Zhou, Ruobing Xie, Lanrui Wang +1

Gated attention is an effective approach to mitigate attention sinks and enhance the representational capacity of attention. To further extend its effectiveness-efficiency Pareto f…

cs.CL2024

Various Lengths, Constant Speed: Efficient Language Modeling with Lightning Attention

Zhen Qin, Weigao Sun, Dong Li +3

We present Lightning Attention, the first linear attention implementation that maintains a constant training speed for various sequence lengths under fixed memory consumption. Due…

cs.CL2024

HGRN2: Gated Linear RNNs with State Expansion

Zhen Qin, Songlin Yang, Weixuan Sun +4

Hierarchically gated linear RNN (HGRN, \citealt{HGRN}) has demonstrated competitive training speed and performance in language modeling while offering efficient inference. However,…

cs.CL20241 cited

CO2: Efficient Distributed Training with Full Communication-Computation Overlap

Weigao Sun, Zhen Qin, Weixuan Sun +5

The fundamental success of large language models hinges upon the efficacious implementation of large-scale distributed training techniques. Nevertheless, building a vast, high-perf…

cs.CL20242 cited

Lightning Attention-2: A Free Lunch for Handling Unlimited Sequence Lengths in Large Language Models

Zhen Qin, Weigao Sun, Dong Li +3

Linear attention is an efficient attention mechanism that has recently emerged as a promising alternative to conventional softmax attention. With its ability to process tokens in l…

cs.CL20232 cited

Linearized Relative Positional Encoding

Zhen Qin, Weixuan Sun, Kaiyue Lu +6

Relative positional encoding is widely used in vanilla and linear transformers to represent positional information. However, existing encoding methods of a vanilla transformer are…