Transferable Modularity in Modular Deep Learning: A Knowledge Distillation Case Study

מושגי ליבה

Transferable modularity in modular deep learning can enhance knowledge transfer between models, even when they are incompatible, through the lens of knowledge distillation.

תקציר

The content explores the concept of transferable modularity in modular deep learning, focusing on the transfer of parameter-efficient fine-tuning (PEFT) modules between different models. The study investigates the potential of transferring modules from larger to smaller models and proposes a method for transferring modules between incompatible models without increasing inference complexity. Experiments on various tasks and languages demonstrate the initial potential of transferable modularity.
1. Introduction

Modular Deep Learning emphasizes the transfer of learning between tasks or languages.
Modules enable efficient fine-tuning of Large Language Models and enhance task-level generalization.
Repositories like AdapterHub promote reusability of pre-trained modules for new use cases.
2. Transferable Modularity

The study aims to transfer PE modules from a teacher to a student model for enhanced fine-tuning.
A method is proposed for transferring modules between matching and incompatible PLMs.
A parameter-free pruning and alignment method is introduced for models with mismatched internal dimensionality.
3. Experiments

Evaluation is conducted on Named Entity Recognition, Paraphrase Identification, and Natural Language Inference tasks across multiple languages.
Two PEFT methods, Adapter and LoRA, are used for fine-tuning multilingual models.
Results show improvements in PEFT when transferring modules from a matching teacher to a student model.
4. Results and Discussion

Matching models show consistent improvement in PEFT with transferred modules.
Incompatible models face alignment challenges, affecting the transfer of knowledge between models.
The study highlights the potential of transferable modularity in modular deep learning.

סטטיסטיקה

Parameter-efficient fine-tuning (PEFT) modularity has been shown to work for various use cases.
Models like BERT and DistilBERT are used for transferring modules between matching PLMs.
A pruning and alignment method is proposed for transferring modules between incompatible PLMs.

ציטוטים

"The effective utilization of transferable modularity can reduce computational burden, especially with the ongoing scaling of Large Language Models."
"The study aims to fill the gap in transferable modularity through Knowledge Distillation, presenting a straightforward approach to transferring pre-trained modules between PLMs."

תובנות מפתח מזוקקות מ:

Is Modularity Transferable? A Case Study through the Lens of Knowledge Distillation

by Mateusz Klim... ב- arxiv.org 03-28-2024

https://arxiv.org/pdf/2403.18804.pdf

שאלות מעמיקות

How can transferable modularity impact the scalability of Large Language Models?

Transferable modularity can significantly impact the scalability of Large Language Models by reducing the computational burden associated with training and fine-tuning these models. By enabling the transfer of task-specific modules between different models, researchers can leverage the knowledge and expertise embedded in larger models and apply it to smaller models. This transferability allows for more efficient parameter usage, faster training times, and improved performance on various tasks. As Large Language Models continue to grow in size and complexity, the ability to transfer modular components between models can enhance the reusability and adaptability of these models across different domains and languages.

What are the limitations of the proposed method for transferring modules between incompatible PLMs?

While the proposed method for transferring modules between incompatible Pre-trained Language Models (PLMs) shows promise, there are several limitations to consider. One major limitation is the challenge of aligning the latent spaces of the teacher and student models when they have different internal dimensionalities. This misalignment can lead to difficulties in mapping the modules effectively, resulting in suboptimal performance during the transfer process. Additionally, the method relies on a correlation-based approach for alignment, which may not always capture the complex relationships between the features learned by the models. Furthermore, the method may struggle with deeper models that have more complex representations, making it less effective for transferring modules between models with significant architectural differences.

How can the concept of transferable modularity be applied to other domains beyond Natural Language Processing?

The concept of transferable modularity can be applied to various domains beyond Natural Language Processing to enhance the efficiency and effectiveness of machine learning models. In computer vision, for example, transferable modularity could enable the transfer of task-specific modules between different image recognition models, allowing for faster adaptation to new tasks or datasets. In healthcare, transferable modularity could facilitate the sharing of specialized modules between medical imaging models, improving diagnostic accuracy and reducing the need for extensive retraining. Additionally, in autonomous vehicles, transferable modularity could enable the transfer of modules for different driving scenarios, enhancing the adaptability and safety of self-driving systems. Overall, the concept of transferable modularity has the potential to revolutionize various domains by promoting knowledge transfer and reusability across different machine learning models.