Continual Prompt Tuning for Lifelong Few-shot Language Learning with Queue-based Prompt Management

Core Concepts

Q-tuning, a novel approach for continual prompt tuning, enables lifelong learning of a pre-trained language model by managing a prompt queue and adaptively aggregating previous prompts.

Abstract

The paper introduces Q-tuning, a novel approach for continual prompt tuning that enables lifelong learning of a pre-trained language model. Key highlights: Q-tuning manages a prompt queue (Q-prompt) that stores previously learned prompts. For a new task, Q-tuning trains a new prompt combined with the fixed Q-prompt. Q-tuning uses an adaptive knowledge aggregation technique to reweigh previous prompts in the queue, enhancing forward knowledge transfer. When the Q-prompt reaches its maximum capacity, Q-tuning leverages a PCA-based eviction rule to reduce the queue size while preserving primary knowledge of old tasks. To mitigate information loss from eviction, Q-tuning proposes a globally shared prefix prompt and a memory retention regularization. Extensive experiments demonstrate Q-tuning outperforms state-of-the-art continual learning and prompt tuning methods, especially on long task sequences that mimic lifelong learning scenarios.

Stats

The paper reports accuracy metrics on various few-shot continual learning benchmarks with short and long task sequences.

Quotes

"Q-tuning manages a Queue-based prompt (Q-prompt), which is stored in a finite-size data buffer." "We design an adaptive knowledge aggregation technique that reweighs previous prompts in the queue with a learnable low-rank matrix." "We leverage a PCA-based eviction rule to reduce the queue's size, allowing the newly trained prompt to be added while preserving the primary knowledge of old tasks."

Key Insights Distilled From

Q-Tuning: Queue-based Prompt Tuning for Lifelong Few-shot Language Learning

by Yanhui Guo,S... at arxiv.org 04-24-2024

https://arxiv.org/pdf/2404.14607.pdf

Deeper Inquiries

How can Q-tuning be extended to handle unknown task identities at test time?

To handle unknown task identities at test time, Q-tuning can be extended by incorporating a trainable query key for each task-specific prompt in the Q-prompt queue. During training, the query key can be jointly trained to maximize the similarity between the key and the feature representation of each sample from the corresponding task. At test time, when presented with an input of unknown identity, the model can use the query key to identify the most relevant task-specific prompt in the queue for inference. This approach allows for adaptive and dynamic selection of the appropriate prompt based on the input data, even when the task identity is not explicitly provided.

What are the potential limitations of the memory retention regularization approach used in Q-tuning?

While the memory retention regularization approach used in Q-tuning is effective in mitigating information loss caused by trimming the Q-prompt queue, there are potential limitations to consider: Overfitting: The regularization technique may lead to overfitting if not properly controlled, especially when the model is trained on a large number of tasks. Balancing the regularization strength is crucial to prevent the model from memorizing noise or irrelevant information. Computational Complexity: Implementing the memory retention regularization adds an additional computational overhead during training, as it involves maximizing the mutual information between the shared prefix prompt and the knowledge learned from previous tasks. This can increase training time and resource requirements. Hyperparameter Sensitivity: The effectiveness of the regularization approach may be sensitive to the choice of hyperparameters, such as the weighting factor for the regularization term. Finding the optimal hyperparameters for different datasets and task sequences can be challenging and time-consuming.

Can the Q-tuning framework be applied to other parameter-efficient fine-tuning techniques beyond prompt tuning?

Yes, the Q-tuning framework can be adapted and applied to other parameter-efficient fine-tuning techniques beyond prompt tuning. The key principles of Q-tuning, such as maintaining a prompt queue, adaptive knowledge aggregation, and memory retention regularization, can be generalized to various fine-tuning approaches that involve continual learning or lifelong learning scenarios. For example, Q-tuning could be extended to work with techniques like feature-based methods, meta-learning approaches, or other lightweight fine-tuning strategies that aim to optimize specific components of a pre-trained model while keeping the rest frozen. By incorporating the core concepts of Q-tuning, these techniques can benefit from improved knowledge transfer, reduced information loss, and enhanced performance on sequential learning tasks.