Meta-Learning Approach Improves Transformer In-Context Learning Using Diverse Small-Scale Datasets
Researchers propose training transformer models for in-context learning using collections of small, domain-specific datasets rather than a single large unstructured corpus. The study uses meta-learning on the Meta-Album dataset collection, testing generalization in controlled, continual, and unsupervised settings. The findings suggest that data quality and diversity can substitute for raw scale, while also reducing privacy risks and storage costs.
A new preprint from arXiv proposes an alternative training paradigm for in-context learning in transformer models, replacing the conventional reliance on large, unstructured datasets with a curated collection of multiple small-scale, domain-specific datasets. The researchers argue that existing large-dataset approaches carry significant drawbacks, including high storage costs, difficulty assessing data quality and balance, and privacy and ethical risks from sensitive information. Using meta-learning on the Meta-Album image collection, the team evaluated their approach across three scenarios: a controlled setting where the test domain was entirely excluded from training, a continual learning scenario testing robustness to catastrophic forgetting, and a more challenging unsupervised setting. Results indicate that models trained on the curated multi-dataset collection achieve generalization performance comparable to those trained on a single large-scale dataset, while demonstrating improved out-of-domain generalization. The authors also highlight practical advantages in modularity and replaceability, meaning individual datasets in the collection can be swapped or updated without retraining from scratch.
What's missing
The study is a preprint and has not yet undergone formal peer review. Key limitations include that experiments are conducted on the Meta-Album image classification benchmark, so generalizability to other modalities such as text or multimodal tasks remains untested. The paper does not extensively address computational costs of the meta-learning procedure itself relative to standard large-dataset training, nor does it compare against recent large-scale foundation models. The scope of 'comparable performance' is not benchmarked against state-of-the-art large language models trained on web-scale corpora.
What different sources said
- arXiv cs.AICenter
Meta-Learning Transformers to Improve In-Context Generalization
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