Task-Adaptive Pretrained Language Models via Clustered-Importance Sampling
David Grangier Simin Fan Skyler Seto Pierre Ablin
Specialist language models (LMs) focus on a specific task or domain on which they often outperform generalist LMs of the same size. However, the specialist data needed to pretrain these models is only available in limited amount for most tasks. In this work, we build specialist models from large generalist training sets instead. We adjust the training distribution of the generalist data with guidance from the limited domain-specific data. We explore several approaches, with clustered importance sampling standing out. This method clusters the generalist dataset and samples from these clusters based on their frequencies in the smaller specialist dataset. It is scalable, suitable for pretraining and continued pretraining, it works well in multi-task settings. Our findings demonstrate improvements across different domains in terms of language modeling perplexity and accuracy on multiple-choice question tasks. We also present ablation studies that examine the impact of dataset sizes, clustering configurations, and model sizes.
task-adaptive pretraining, language models, importance sampling, domain adaptation
Scalable clustered importance sampling for LM pretraining: End-task accuracy is improved when pretraining on a generalist dataset resampled with task-specific importance sampling weights.