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Structure-inducing pre-training
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DOI：10.1038/s42256-023-00647-z.png)
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Abstract
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Designing methods to induce explicit and deep structural constraints in latent space at the sample level is an open problem in natural language processing-derived methods relying on transfer learning. McDermott and colleagues propose and analyse a pre-training framework imposing such structural constraints, and empirically demonstrate its advantages by showing that it outperforms existing pre-training state-of-the-art methods. Language model pre-training and the derived general-purpose methods have reshaped machine learning research. However, there remains considerable uncertainty regarding why pre-training improves the performance of downstream tasks. This challenge is pronounced when using language model pre-training in domains outside of natural language. Here we investigate this problem by analysing how pre-training methods impose relational structure in induced per-sample latent spaces-that is, what constraints do pre-training methods impose on the distance or geometry between the pre-trained embeddings of samples. A comprehensive review of pre-training methods reveals that this question remains open, despite theoretical analyses showing the importance of understanding this form of induced structure. Based on this review, we introduce a pre-training framework that enables a granular and comprehensive understanding of how relational structure can be induced. We present a theoretical analysis of the framework from the first principles and establish a connection between the relational inductive bias of pre-training and fine-tuning performance. Empirical studies spanning three data modalities and ten fine-tuning tasks confirm theoretical analyses, inform the design of novel pre-training methods and establish consistent improvements over a compelling suite of methods.
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