Efficient Model Adaptation via Prompt Transfer and Knowledge Distillation

Kernekoncepter

A novel prompt transfer approach (PANDA) that leverages knowledge distillation to effectively transfer knowledge from source prompts to target prompts, outperforming vanilla prompt transfer methods.

Resumé

The paper proposes a new approach called PANDA (Prompt trAnsfer via kNowledge DistillAtion) to improve the efficiency of model adaptation via prompt transfer.
Key highlights:

The vanilla prompt transfer (PoT) approach is sensitive to the similarity between source and target tasks, and directly fine-tuning the prompt initialized with source prompt on the target task can lead to forgetting of useful general knowledge.
PANDA introduces a new metric to better predict the prompt transferability between source and target tasks, and leverages knowledge distillation to transfer knowledge from source prompts to the target prompt in a subtle manner, alleviating the problem of prior knowledge forgetting.
Extensive experiments on 189 combinations of 21 source and 9 target datasets across 5 scales of pre-trained language models (PLMs) demonstrate that PANDA consistently outperforms the vanilla PoT approach by 2.3% average score (up to 24.1%) and enables prompt-tuning to achieve competitive or even better performance than model-tuning in various PLM scale scenarios.

Statistik

The training dataset sizes range from 400 (COPA) to 393K (MNLI).
The authors use learning rates from 5e-3 to 1e-2, batch sizes from 16 to 64, and training epochs from 20 to 100 for different datasets.

Citater

"Prompt-tuning can achieve competitive performance against model-tuning when the PLM exceeds billions of parameters [8], but there are still some gaps between prompt-tuning and model-tuning at smaller PLM scales [11], [9], which can also be observed from our empirical results in Figure 1."
"To this end, we first propose a new metric to better predict the prompt transferability, and then improve the Prompt trAnsfer via kNowledge DistillAtion (PANDA for short)."
"Extensive and systematic experiments on 189 pairs of source-target tasks across 5 scales of PLMs prove the effectiveness of our methods."

Vigtigste indsigter udtrukket fra

PANDA

by Qihuang Zhon... kl. arxiv.org 04-03-2024

https://arxiv.org/pdf/2208.10160.pdf

Dybere Forespørgsler

How can the proposed PANDA approach be further extended to handle more complex transfer learning scenarios, such as multi-task or cross-lingual settings

The PANDA approach can be extended to handle more complex transfer learning scenarios by incorporating it into multi-task or cross-lingual settings.
Multi-task Settings:
In multi-task settings, PANDA can be adapted to transfer knowledge from multiple source tasks to a target task simultaneously. This can be achieved by fusing the knowledge from different source prompts using an early-fusion or late-fusion strategy. The early-fusion strategy involves directly combining multiple soft prompts as an ensemble teacher prompt, while the late-fusion strategy aggregates multiple teacher representations to create a weighted average for supervision in knowledge distillation. By leveraging the knowledge distillation technique in a multi-task framework, PANDA can effectively transfer diverse knowledge from multiple tasks to enhance performance on the target task.
Cross-lingual Settings:
For cross-lingual settings, PANDA can be extended to transfer knowledge between languages by training on source tasks in one language and transferring the learned knowledge to a target task in a different language. This can involve mapping the semantic space of tasks across languages and adapting the knowledge distillation process to account for language differences. By incorporating language-specific considerations and alignment techniques, PANDA can facilitate effective knowledge transfer in cross-lingual scenarios.

What are the potential limitations or drawbacks of the knowledge distillation technique used in PANDA, and how can they be addressed in future work

While the knowledge distillation technique used in PANDA is effective in transferring knowledge from a teacher network to a student network, there are potential limitations and drawbacks that should be considered for future work:
Limitations:

Overfitting: Knowledge distillation may lead to overfitting if the student network memorizes the teacher's predictions without truly understanding the underlying concepts.
Loss of Diversity: The distilled knowledge may lack the diversity and richness of the original knowledge, potentially limiting the student network's ability to generalize to unseen data.
Sensitivity to Hyperparameters: The performance of knowledge distillation can be sensitive to hyperparameters such as the balance between the classification loss and distillation loss.

Addressing Limitations:

Regularization Techniques: Introducing regularization techniques like dropout or weight decay can help prevent overfitting during knowledge distillation.
Ensemble Methods: Combining multiple teacher networks or using ensemble methods can enhance the diversity of distilled knowledge and improve generalization.
Hyperparameter Tuning: Conducting thorough hyperparameter tuning and sensitivity analysis can help optimize the performance of knowledge distillation and mitigate its sensitivity to hyperparameters.

By addressing these limitations and incorporating strategies to overcome potential drawbacks, future work can further enhance the effectiveness and robustness of the knowledge distillation technique in PANDA.

Given the success of PANDA in improving prompt-tuning, how can the insights from this work be applied to enhance other parameter-efficient fine-tuning methods for pre-trained language models

The success of PANDA in improving prompt-tuning can be applied to enhance other parameter-efficient fine-tuning methods for pre-trained language models by leveraging the following insights:

Knowledge Distillation: Implementing knowledge distillation in other fine-tuning methods can facilitate effective knowledge transfer from pre-trained models to task-specific prompts or modules. By distilling the knowledge learned from a teacher network into a student network, models can benefit from the distilled expertise while maintaining efficiency.

Transfer Learning Strategies: Incorporating transfer learning strategies, such as prompt transfer, can improve the adaptation of pre-trained models to new tasks. By initializing task-specific prompts with knowledge distilled from related source tasks, models can achieve better performance and faster convergence on target tasks.

Multi-task Learning: Extending the principles of multi-task learning, where models are trained on multiple related tasks simultaneously, can enhance the generalization and efficiency of fine-tuning methods. By jointly optimizing models for multiple tasks, shared knowledge and representations can be leveraged to improve performance across a range of NLP tasks.

By applying these insights and techniques inspired by PANDA, other parameter-efficient fine-tuning methods can be enhanced to achieve better performance, faster convergence, and improved generalization on diverse NLP tasks.