This AI article from Google DeepMind studies the gap between pre-training data composition and in-context learning in pre-trained transformers

Google DeepMind researchers explore the in-context learning (ICL) capabilities of large language models, specifically transformative ones, trained on various task families. However, their study needs to work on out-of-domain tasks, which reveals limitations in generalization to features beyond the pre-training distribution. The findings suggest that the impressive ICL capabilities of high-capacity sequence models depend more on pre-training data coverage than on inherent inductive biases for fundamental generalization.

The study examines the ability of transformer models to perform few-shot learning using ICL. Highlights the impact of pre-training data on model performance. The study shows that transformers perform well in unsupervised model selection when pre-training data adequately covers task families. However, they face limitations and reduced generalization when faced with out-of-domain tasks. It reveals that models trained on mixtures of feature classes perform almost as well as those trained exclusively on one class. The study includes ICL learning curves that illustrate the performance of the models on various pre-training data compositions.

The research delves into the ICL capabilities of transformative models, emphasizing their ability to learn tasks within and beyond pre-training distributions. Transformers show impressive learning in just a few attempts, excelling in handling nonlinear and high-dimensional functions. The study focuses on how pre-training data influences these capabilities in a controlled environment, with the goal of understanding the impact of building data sources. It evaluates the model’s competence in selecting between families of feature classes observed in pre-training and investigates generalization outside the distribution. Performance evaluations include tasks not seen during training and extreme variations of functions seen before training.

In a controlled study, the study uses transformer models trained on (x, f(x)) pairs, not natural language, to examine the impact of pre-training data on few-chance learning. By comparing models with various pre-training data compositions, the research evaluates their performance on different evaluation functions. By analyzing model selection across families of feature classes and exploring out-of-distribution generalization, the study incorporates ICL curves, which show the mean squared error for various compositions of pre-training data. Evaluations of tasks inside and outside the pre-training distribution reveal empirical evidence of failure modes and diminished generalization.

Transformer models exhibit near-optimal unsupervised selection within well-represented task families from pre-training data. However, when faced with tasks outside of their pre-training data, they manifest multiple failure modes and decreased generalization. Model comparisons between different pre-training data compositions reveal that models trained on a diverse data mix perform almost as well as those pre-trained exclusively on one feature class. The study presents the root mean square difference metric, normalized by the differences between sparse and dense models, emphasizing the importance of pre-training data coverage on inductive biases for fundamental generalization capabilities.

In conclusion, the composition of pre-training data plays a crucial role in the accurate selection of transformative models, particularly in natural language environments. While these models can learn new tasks without explicit training, they may need help handling loads beyond the pre-training data, resulting in multiple failure modes and reduced generalization. Therefore, it is essential to understand and enable ICL to improve the overall effectiveness of these models.

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