Researchers from Google DeepMind conducted a study on the in-context learning capabilities of large language models, specifically transformers. The study found that transformers perform well in tasks within the pretraining data but face limitations and reduced generalization when dealing with out-of-domain tasks. The research emphasizes the importance of pretraining data coverage over inductive biases for generalization.
Study Highlights Gap Between Pretraining Data Composition and In-Context Learning in Pretrained Transformers
Researchers from Google DeepMind have conducted a study exploring the capabilities of large language models, specifically transformers, in in-context learning (ICL). The study focuses on the impact of pretraining data on the models’ performance and reveals limitations in generalization for tasks beyond the pretraining distribution.
Key Findings:
– Transformers perform well in unsupervised model selection when the pretraining data adequately covers the task families.
– However, they face limitations and reduced generalization when dealing with out-of-domain tasks.
– Models trained on mixtures of function classes perform almost as well as those trained exclusively on one class.
– ICL learning curves illustrate the performance of the models across various pretraining data compositions.
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