Self-supervised Meta-Prompt Learning with Meta-Gradient Regularization for Few-shot Generalization (2303.12314v4)

Abstract: Prompt tuning is a parameter-efficient method, which learns soft prompts and conditions frozen LLMs to perform specific downstream tasks. Though effective, prompt tuning under few-shot settings on the one hand heavily relies on a good initialization of soft prompts. On the other hand, it can easily overfit to few-shot training samples, thereby undermining generalizability. Existing works leverage pre-training or supervised meta-learning to initialize soft prompts but they fail to data-efficiently generalize to unseen downstream tasks. To address the above problems, this paper proposes a novel Self-sUpervised meta-Prompt learning framework with MEta-gradient Regularization for few-shot generalization (SUPMER). SUPMER leverages self-supervised meta-learning with a diverse set of well-designed meta-training tasks to learn a universal prompt initialization for efficient adaptation using only unlabeled data. Additionally, it jointly meta-learns a gradient regularization function to transform raw gradients into a domain-generalizable direction, thus alleviating the problem of overfitting. Extensive experiments show that SUPMER achieves better performance for different few-shot downstream tasks, and also exhibits a stronger domain generalization ability. The code for SUPMER will be available at https://github.com/beepkh/SUPMER.

Self-supervised Meta-Prompt Learning with Meta-Gradient Regularization for Few-shot Generalization

The paper presents SUPMER, a framework focused on enhancing the few-shot learning capabilities of prompt-tuning methods in NLP. Traditional prompt tuning techniques, while parameter-efficient, often stumble upon an issue: they can overfit to limited training samples and rely heavily on the initialization of soft prompts. SUPMER addresses these issues by leveraging self-supervised meta-learning and introducing a meta-gradient regularization mechanism.

Conceptual Framework

SUPMER builds upon the novel integration of self-supervised meta-learning into the prompt tuning paradigm. This is achieved by creating a diverse array of meta-training tasks designed to harness unlabeled data efficiently. Unlike existing methods that utilize pre-training or supervised meta-learning, this approach aims to provide a more generalized and robust framework. The main objective is to derive a universal prompt initialization that can efficiently adapt to various downstream tasks, even under few-shot conditions.

Moreover, SUPMER incorporates a meta-gradient regularization function into its meta-learning process. This function plays a pivotal role in transforming raw gradients obtained during training into directions that are deemed domain-generalizable. By optimizing this regularization function simultaneously with prompt training, the framework discourages overfitting from the gradient perspective, an innovation not considered in prior models.

Experimental Setup and Results

Extensive experiments show the superiority of SUPMER over existing baseline methods in few-shot learning and domain generalization across numerous tasks. The paper demonstrates that SUPMER is not only capable of outperforming conventional prompt tuning methods but also exceeds the performance of full-model fine-tuning on some problems. Such results suggest that efficient soft prompt initializations obtained through self-supervised tasks can enhance the model's performance significantly. Furthermore, the results boast improved domain generalization, offering evidence that the meta-gradient regularization successfully mitigates the overfitting challenge.

Implications and Future Directions

This research holds potential implications for both the development of NLP systems and the broader AI landscape. Practically, SUPMER sets the stage for more efficient and adaptable NLP models that perform well on tasks with limited labeled data and exhibit robust generalization across domains. Theoretically, it provides a bridge between prompt-based tuning and meta-learning, proposing a new perspective on how task-agnostic knowledge can be harnessed using unlabeled data.

Future research may delve into extending this framework to large-scale LLMs or multilingual settings to test its scalability and generalizability. Moreover, exploring the inclusion of additional modalities, such as combining vision and text, could further expand the applicability of SUPMER within the context of multimodal tasks.

In conclusion, SUPMER presents an effective and practical approach to improving few-shot learning capabilities in NLP models, standing as a testament to the continued evolution in efficiently leveraging vast amounts of unlabeled data within meta-learning frameworks.