Meta-Learning Approach Improves Transformer In-Context Learning Using Diverse Small-Scale Datasets
Researchers propose training transformer models for in-context learning using multiple small-scale, domain-specific datasets instead of large unstructured datasets. The approach, tested on the Meta-Album collection, achieves comparable performance to large-scale training while improving generalization across domains. This matters because it addresses privacy, ethical, and practical concerns associated with large-scale dataset collection while maintaining model effectiveness.
A new training strategy for transformer models leverages meta-learning to improve in-context generalization by using curated collections of small-scale, domain-specific datasets rather than large, unstructured datasets. The researchers tested their approach on the Meta-Album collection across multiple settings: controlled environments with completely excluded test domains, continual learning scenarios with limited information access, and unsupervised settings. Results demonstrate that transformers trained this way achieve comparable performance to models trained on single large-scale datasets while offering advantages in modularity and replaceability. The approach addresses significant practical limitations of conventional training paradigms, including high storage costs, difficulty evaluating data quality and balance, and privacy and ethical concerns from including sensitive information. The findings suggest that data quality and diversity from curated collections can outweigh the scale advantages of large unstructured datasets for in-context learning tasks.
What's missing
The paper does not discuss computational costs or training time comparisons between the proposed approach and large-scale dataset training, which would be relevant for practical adoption. Additionally, specific details about which domains in Meta-Album showed the largest generalization improvements are not provided in the abstract.
What different sources said
- arXiv cs.AICenter
Meta-Learning Transformers to Improve In-Context Generalization
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