Self-Supervised Meta-Learning for Few-Shot Natural Language Classification Tasks (2009.08445v2)

Abstract: Self-supervised pre-training of transformer models has revolutionized NLP applications. Such pre-training with LLMing objectives provides a useful initial point for parameters that generalize well to new tasks with fine-tuning. However, fine-tuning is still data inefficient -- when there are few labeled examples, accuracy can be low. Data efficiency can be improved by optimizing pre-training directly for future fine-tuning with few examples; this can be treated as a meta-learning problem. However, standard meta-learning techniques require many training tasks in order to generalize; unfortunately, finding a diverse set of such supervised tasks is usually difficult. This paper proposes a self-supervised approach to generate a large, rich, meta-learning task distribution from unlabeled text. This is achieved using a cloze-style objective, but creating separate multi-class classification tasks by gathering tokens-to-be blanked from among only a handful of vocabulary terms. This yields as many unique meta-training tasks as the number of subsets of vocabulary terms. We meta-train a transformer model on this distribution of tasks using a recent meta-learning framework. On 17 NLP tasks, we show that this meta-training leads to better few-shot generalization than language-model pre-training followed by finetuning. Furthermore, we show how the self-supervised tasks can be combined with supervised tasks for meta-learning, providing substantial accuracy gains over previous supervised meta-learning.

Overview of Self-Supervised Meta-Learning for Few-Shot Natural Language Classification Tasks

The paper explores the intricacies of self-supervised meta-learning for addressing few-shot natural language classification tasks. The authors have targeted a critical aspect of NLP models—the inefficiency of fine-tuning when tasked with limited labeled examples. The paper introduces a novel approach whereby self-supervised tasks are utilized to create a meta-learning framework, which greatly enhances the generalization capabilities of models in few-shot learning scenarios.

Key Contributions and Methodology

1. Subset Masked LLMing Tasks (SMLMT): The core proposition is the creation of SMLMT, a framework derived from unlabeled text data that structures classification tasks around subsets of vocabulary terms. This method parallelizes the familiar cloze test format into a meta-learning architecture, thus generating a broad distribution of tasks without the need for extensive supervised datasets.
2. Task Distribution and Meta-Training Approach: By employing transformers as the foundational architecture and leveraging optimized meta-learning techniques, the paper establishes a new meta-training protocol. The approach allows for effective parameter learning, which is tuned for adaptation to new tasks even with minimal exposure to labeled data.
3. Hybrid Learning Framework: The research extends meta-training by combining SMLMT with supervised tasks, demonstrating significant accuracy gains over conventional supervised meta-learning strategies. This hybrid method mitigates meta-overfitting due to the diverse nature of generated tasks, optimally balancing the benefits of both self-supervised and supervised data.
4. Evaluation: Empirically, the proposed approach showcases better few-shot generalization across 17 NLP tasks, achieving substantial gains compared to previously established benchmarks in NLP pre-training and finetuning paradigms.

Numerical Results and Discussion

The empirical analysis confirms that self-supervised meta-learning significantly advances few-shot learning. The hybrid framework sees improvements up to 21% over traditional multi-task models. The authors meticulously examine representations and adaptation speeds with varying model sizes, asserting the effectiveness of larger models in generalization post meta-training.

Implications and Future Prospects

This research serves as a catalyst for exploring large-scale applications of meta-learning in NLP contexts. By demonstrating the capability of transformer models to learn efficiently from both self-supervised and supervised cues, the paper lays the groundwork for further innovations in meta-learning—to include avenues such as neural architecture search, continual learning, and hyper-parameter optimization. Future investigations could build upon this foundation, extending its application to broader AI fields where few-shot learning remains a critical challenge.

This paper offers vital insights into optimizing LLM efficiency and stands as a testament to the evolving capabilities of self-supervised and meta-learning methodologies within NLP.