Improving Cross-Lingual Generalization of Adapter-Based Language Models with Scheduled Unfreezing

Core Concepts

Scheduled unfreezing methods, such as Gradual Unfreezing (GU) and Linear Probing then Fine-Tuning (LPFT), can improve the cross-lingual generalization performance of adapter-based language models, even in a catastrophic forgetting-free setting.

Abstract

The paper investigates the use of scheduled unfreezing methods to improve the cross-lingual generalization of adapter-based language models. The key insights are: Scheduled unfreezing techniques, such as GU and LPFT, can effectively close the performance gap between adapter-based fine-tuning and full fine-tuning of language models like mBERT and XLM-R on cross-lingual tasks. This suggests that scheduled unfreezing can do more than just mitigate catastrophic forgetting. The authors analyze the learning dynamics during adapter training using the trace of the Fisher Information Matrix (tr(F)). They find that scheduled unfreezing changes the tr(F) dynamics compared to standard fine-tuning, and that the tr(F) dynamics correlate with cross-lingual generalization performance. Inspired by these findings, the authors propose a tr(F)-based scheduled unfreezing algorithm (FUN) that achieves comparable or better performance than heuristic-based methods like GU, providing a more principled approach to selecting the unfreezing schedule. The authors also show that the benefits of scheduled unfreezing extend to other adapter types, such as LoRA, demonstrating the generality of their findings.

Stats

"Standard fine-tuning of language models typically performs well on in-distribution data, but suffers with generalization to distribution shifts." "Adapters insert a small number of trainable parameters into a frozen pretrained multilingual language model (e.g., mBERT, XLM-R) to achieve positive transfer while avoiding catastrophic forgetting." "Gradual unfreezing (GU) was previously proposed for general transfer learning of in-distribution data in monolingual contexts in NLP, and has been predominantly applied to full fine-tuning." "Linear-Probing-then-Fine-Tuning (LPFT) was proposed for transfer learning of both in-distribution and distribution-shifted evaluation data using full fine-tuning in computer vision."

Quotes

"Scheduled unfreezing methods have shown promising transfer learning results. However, it is unclear whether scheduled unfreezing can do more than just mitigate CF, and benefit CF-free methods and cross-lingual transfer (which is a different type of distribution shift than previously studied)." "Our experiments show that scheduled unfreezing methods close the gap to full fine-tuning and achieve stronger cross-lingual transfer performance, suggesting that these methods can go beyond just mitigating catastrophic forgetting." "Our experiments reveal that scheduled unfreezing induces different learning dynamics compared to standard fine-tuning, and provide evidence that the dynamics of Fisher Information during training correlate with cross-lingual generalization performance."

Key Insights Distilled From

FUN with Fisher

by Chen... at arxiv.org 04-05-2024

https://arxiv.org/pdf/2301.05487.pdf

Deeper Inquiries

How can the insights from this work on scheduled unfreezing and Fisher Information be applied to improve the cross-lingual generalization of large language models beyond just adapter-based models

The insights gained from the work on scheduled unfreezing and Fisher Information can be applied to improve the cross-lingual generalization of large language models beyond just adapter-based models by incorporating similar training dynamics and metrics. One way to extend these insights is to apply scheduled unfreezing methods to other types of language models, such as transformer-based models like BERT, RoBERTa, or GPT models. By implementing scheduled unfreezing algorithms and monitoring the Fisher Information dynamics during training, researchers can optimize the learning process to enhance cross-lingual transfer performance. Additionally, exploring the relationship between tr(F) and generalization in different model architectures can provide valuable insights into improving cross-lingual capabilities across a wider range of models.

Can the tr(F)-based scheduled unfreezing algorithm (FUN) be further improved or extended to handle non-uniform unfreezing schedules, and how would that impact the cross-lingual transfer performance

The tr(F)-based scheduled unfreezing algorithm (FUN) can be further improved or extended to handle non-uniform unfreezing schedules by incorporating adaptive learning rate strategies or dynamic unfreezing criteria based on the Fisher Information dynamics. By introducing non-uniform unfreezing schedules, researchers can potentially optimize the training process to focus on specific layers or components of the model that are more critical for cross-lingual generalization. This approach could lead to more efficient training and better performance in handling distribution shifts in cross-lingual transfer tasks. Experimenting with different unfreezing schedules and adapting them based on the dynamics of tr(F) could provide valuable insights into further enhancing the cross-lingual transfer capabilities of large language models.

What other factors, beyond the unfreezing schedule and tr(F) dynamics, might influence the cross-lingual generalization capabilities of adapter-based and full fine-tuned language models

Beyond the unfreezing schedule and tr(F) dynamics, several other factors may influence the cross-lingual generalization capabilities of adapter-based and full fine-tuned language models. Some of these factors include: Data Augmentation Techniques: Implementing effective data augmentation strategies specific to cross-lingual tasks can help improve model robustness and generalization. Regularization Methods: Utilizing regularization techniques such as dropout, weight decay, or early stopping can prevent overfitting and enhance the model's ability to generalize across languages. Model Architecture: Exploring different model architectures or modifications to existing architectures can impact cross-lingual transfer performance. For example, incorporating multi-lingual embeddings or attention mechanisms tailored for cross-lingual tasks. Optimization Algorithms: Experimenting with different optimization algorithms and learning rate schedules can optimize the training process and improve the model's ability to generalize across languages. Task-Specific Fine-Tuning: Tailoring the fine-tuning process based on the specific characteristics of the target task or language can lead to better cross-lingual transfer performance. Considering these factors in conjunction with the unfreezing schedule and tr(F) dynamics can provide a comprehensive approach to enhancing the cross-lingual generalization capabilities of large language models.