Meta-Learning Adaptable Foundation Models
Keywords: meta-learning theory, parameter efficient fine-tuning, low-rank adaptation, foundation models, non-convex optimization
TL;DR: We analyze meta-learning infused with parameter efficient fine-tuning to prepare foundation models for downstream adaptation and provide theory and experiments demonstrating the benefits of our proposed meta-learning approach.
Abstract: The power of foundation models (FMs) lies in their capacity to learn highly expressive representations that can be adapted to a broad spectrum of tasks. However, these pretrained models require multiple stages of fine-tuning to become effective for downstream applications. Conventionally, the model is first retrained on the aggregate of a diverse set of tasks of interest and then adapted to specific low-resource downstream tasks by utilizing a parameter-efficient fine-tuning (PEFT) scheme. While this procedure seems reasonable, the independence of the retraining and fine-tuning stages causes a major issue, as there is no guarantee the retrained model will achieve good performance post-fine-tuning. To explicitly address this issue, we introduce a meta-learning framework infused with PEFT in this intermediate retraining stage to learn a model that can be easily adapted to unseen tasks. For our theoretical results, we focus on linear models using low-rank adaptations. In this setting, we demonstrate the suboptimality of standard retraining for finding an adaptable set of parameters. Further, we prove that our method recovers the optimally adaptable parameters. We then apply these theoretical insights to retraining the RoBERTa model to predict the continuation of conversations between different personas within the ConvAI2 dataset. Empirically, we observe significant performance benefits using our proposed meta-learning scheme during retraining relative to the conventional approach.
Supplementary Material: zip
Primary Area: transfer learning, meta learning, and lifelong learning
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Submission Number: 12900
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