Mixture of Scales: Memory-Efficient Token-Adaptive Binarization for
  Large Language Models
Authors: name: Dongwon Jo, name: Taesu Kim, name: Yulhwa Kim, name: Jae-Joon Kim
Organization: Seoul National University

Binarization, which converts weight parameters to binary values, has emerged
as an effective strategy to reduce the size of large language models (LLMs).
However, typical binarization techniques significantly diminish linguistic
effectiveness of LLMs. To address this issue, we introduce a novel binarization
technique called Mixture of Scales (BinaryMoS). Unlike conventional methods,
BinaryMoS employs multiple scaling experts for binary weights, dynamically
merging these experts for each token to adaptively generate scaling factors.
This token-adaptive approach boosts the representational power of binarized
LLMs by enabling contextual adjustments to the values of binary weights.
Moreover, because this adaptive process only involves the scaling factors
rather than the entire weight matrix, BinaryMoS maintains compression
efficiency similar to traditional static binarization methods. Our experimental
results reveal that BinaryMoS surpasses conventional binarization techniques in
various natural language processing tasks and even outperforms 2-bit
quantization methods, all while maintaining similar model size to static
binarization techniques.