2 citations · 5 across the 5 of their papers we have counts for
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cs.LG2024
Brain-inspired continual pre-trained learner via silent synaptic consolidation
Xuming Ran, Juntao Yao, Yusong Wang +2
Pre-trained models have demonstrated impressive generalization capabilities, yet they remain vulnerable to catastrophic forgetting when incrementally trained on new tasks. Existing…
cs.CL2024★ 2 cited
FlatQuant: Flatness Matters for LLM Quantization
Yuxuan Sun, Ruikang Liu, Haoli Bai +10
Recently, quantization has been widely used for the compression and acceleration of large language models (LLMs). Due to the outliers in LLMs, it is crucial to flatten weights and…
cs.CL2024★ 2 cited
IntactKV: Improving Large Language Model Quantization by Keeping Pivot Tokens Intact
Ruikang Liu, Haoli Bai, Haokun Lin +6
Large language models (LLMs) excel in natural language processing but demand intensive computation. To mitigate this, various quantization methods have been explored, yet they comp…