Multilingual Large Language Models Can Be Effectively Compressed Without Compromising Performance Across Languages

Core Concepts

Multilingual Brain Surgeon (MBS) is a novel calibration data sampling method that enables effective compression of multilingual large language models while preserving performance across diverse languages.

Abstract

The paper introduces Multilingual Brain Surgeon (MBS), a novel approach for compressing multilingual large language models (LLMs) that addresses the limitations of existing English-centric compression techniques.
Key highlights:

Existing compression methods like GPTQ, SparseGPT, and Wanda rely on a single-language (English) calibration dataset, leading to significant performance degradation for low-resource languages in multilingual models.
MBS overcomes this issue by sampling calibration data from various languages proportionally to their representation in the model's training dataset.
Experiments on the BLOOM multilingual LLM demonstrate that MBS improves the performance of existing compression methods, especially for low-resource languages.
The authors also uncover the dynamics of language interaction during compression, revealing that the larger the proportion of a language in the training set and the more similar the language is to the calibration language, the better the performance it retains after compression.
MBS presents an innovative approach to compressing multilingual LLMs, addressing the performance disparities and improving the language inclusivity of existing compression techniques.

Stats

The BLOOM model was trained on a dataset containing over 100 languages, with English having the largest proportion and Igbo having the smallest.
The experiments were conducted on a subset of 20 languages available in the CC-100 and XL-Sum datasets.
The perplexity of the compressed models was evaluated on the XL-Sum dataset, which covers 45 languages.

Quotes

"MBS overcomes the English-centric limitations of existing methods by sampling calibration data from various languages proportionally to the language distribution of the model training datasets."
"We also uncover the dynamics of language interaction during compression, revealing that the larger the proportion of a language in the training set and the more similar the language is to the calibration language, the better performance the language retains after compression."

Key Insights Distilled From

Multilingual Brain Surgeon

by Hongchuan Ze... at arxiv.org 04-09-2024

https://arxiv.org/pdf/2404.04748.pdf

Deeper Inquiries

How can the proposed MBS approach be extended to other types of multilingual models beyond large language models?

The Multilingual Brain Surgeon (MBS) approach can be extended to other types of multilingual models beyond large language models by adapting the calibration data sampling method to suit the specific characteristics of the models. For instance, in the case of multilingual image recognition models, the calibration data could be sampled from images representing different languages proportionally to their distribution in the training dataset. Similarly, for multilingual speech recognition models, the calibration data could be sampled from audio recordings in various languages based on their representation in the training set. By customizing the sampling method to the data modalities and characteristics of different multilingual models, the MBS approach can be effectively applied to enhance the compression and performance of a wide range of multilingual models.

What are the potential trade-offs or limitations of the MBS approach, and how can they be addressed?

One potential trade-off of the MBS approach is the computational overhead involved in sampling calibration data from multiple languages and ensuring proportional representation. This could lead to increased processing time and resource requirements, especially for models with a large number of languages in the training set. To address this limitation, optimization techniques such as parallel processing and efficient data sampling algorithms can be implemented to streamline the calibration data sampling process and reduce computational costs.
Another limitation of the MBS approach could be the need for a diverse and representative training dataset that includes sufficient data from each language. In scenarios where certain languages are underrepresented in the training data, the performance of the MBS approach may be compromised. To mitigate this limitation, techniques such as data augmentation, transfer learning from related languages, or synthetic data generation can be employed to ensure a more balanced representation of languages in the training dataset.

What other factors, beyond language similarity and training data proportion, might influence the performance of compressed multilingual models, and how can they be incorporated into the MBS framework?

Several other factors can influence the performance of compressed multilingual models, including language complexity, linguistic diversity, and domain-specific characteristics. To incorporate these factors into the MBS framework, additional metrics and features can be considered during the calibration data sampling process. For example, language complexity metrics such as vocabulary size, syntactic complexity, and morphological richness can be used to prioritize calibration data sampling for languages with more intricate linguistic structures.
Furthermore, domain-specific information such as the prevalence of certain topics or domains in different languages can be taken into account to tailor the calibration data sampling to the specific needs of the multilingual model. By integrating a comprehensive set of factors that impact model performance, the MBS framework can be enhanced to provide more nuanced and effective compression for a wide range of multilingual models.