Mixture of Scales: Memory-Efficient Token-Adaptive Binarization for Large Language Models

TL;DR

BinaryMoS is a novel token-adaptive binarization method for large language models that enhances their performance and efficiency by dynamically merging multiple scaling experts, outperforming traditional binarization and 2-bit quantization.

Contribution

Introduces BinaryMoS, a new binarization technique employing multiple scaling experts for adaptive, context-aware weight binarization in large language models.

Findings

BinaryMoS outperforms conventional binarization methods in NLP tasks.

BinaryMoS surpasses 2-bit quantization performance.

Maintains similar compression efficiency to static binarization.

Abstract

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.