Simple, zero overhead way to compress model, KV cache via Low-Rank Decompositionjeffreywong20.github.io1 points·by thw20·2 tháng trước·0 comments
Towards understanding multiple attention sinks in LLMsgithub.com1 points·by thw20·4 tháng trước·2 comments
The Existence and Behavior of Secondary Attention Sinksarxiv.org1 points·by thw20·5 tháng trước·0 comments
thw20·3 tháng trước·discussGood work! This is very interesting. Here's a related work that construct low-rank approximation for attention: https://arxiv.org/abs/2505.12942.Maybe the idea of Query calibration matrix Rxx is of interest to the author!
thw20·4 tháng trước·discussThe up to date paper documenting and analysing the observation is now available on arxiv!
thw20·4 tháng trước·discussThis project reveals an interesting phenomena, where LLM converts semantic non-informative tokens to attention sinks through middle layer MLP.The converted sinks are termed secondary attention sinks as they are weaker then BOS attention sinks.This might be related to layer specialisation in LLM!
thw20·4 tháng trước·discussThis is so amazing. What a masterpiece for intro to reinforcement learning in llm.
Maybe the idea of Query calibration matrix Rxx is of interest to the author!