Core Concepts
Applying Jacobian and Hessian regularization to the intermediate representations of pre-trained language models can significantly improve their generalization capabilities and uncertainty quantification.
Abstract
The paper investigates the role of representation smoothness, achieved via Jacobian and Hessian regularization, in enhancing the performance of pre-trained language models (PLMs). The authors introduce a novel two-phase regularization approach, JACHESS, which minimizes the norms of the Jacobian and Hessian matrices within PLM intermediate representations relative to their inputs.
The key highlights and insights are:
Robustness, defined as the amount that the loss can vary with respect to changes in the inputs, offers an effective lens for understanding generalization in neural networks.
Promoting smooth representations not only aids in better generalization but also supports more reliable uncertainty quantification in the model's predictions.
The authors adapt and expand upon the regularization techniques used in the representation space within computer vision, applying them to natural language processing (NLP) tasks.
JACHESS leverages the continuous embedding space of PLMs to employ representation-based regularization with respect to the inputs, addressing the challenge posed by the discrete nature of data processed by PLMs.
The authors evaluate JACHESS using the GLUE benchmark, demonstrating that it significantly improves in-domain generalization and calibration in PLMs, outperforming unregularized fine-tuning and other similar regularization methods.
JACHESS also enhances the models' ability to quantify uncertainty, yielding more reliable predictions as measured by the Brier score.
Stats
The paper does not provide any specific numerical data or statistics. The focus is on the conceptual and methodological contributions.
Quotes
"Enhancing generalization and uncertainty quantification in pre-trained language models (PLMs) is crucial for their effectiveness and reliability."
"Promoting smooth representations emerges as a promising strategy for boosting generalization through robustness and stabilizing uncertainty quantification in neural networks."
"JACHESS doubly enhances model robustness by reducing sensitivity to input changes and smoothing the curvature of representations."