Fine-Tuning Pre-trained Language Models for Robust Causal Representation Learning

Jialin Yu, Yuxiang Zhou, Yulan He, Nevin L. Zhang, Ricardo Silva

Keywords: representation learning, causal inference, robust machine learning

TL;DR: We describe a way of identifying and constructing robust causal representations leveraging pre-trained language models.

Abstract: The fine-tuning of pre-trained language models (PLMs) has been shown to be effective across various domains. By using domain-specific supervised data, the general-purpose representation derived from PLMs can be transformed into a domain-specific representation. However, these methods often fail to generalize to out-of-domain (OOD) data due to their reliance on $\textit{non-causal}$ representations, often described as spurious features. Existing methods either make use of adjustments with strong assumptions about lack of hidden common causes, or mitigate the effect of spurious features using multi-domain data. In this work, we investigate how fine-tuned pre-trained language models aid generalizability from single-domain scenarios under mild assumptions, targeting more general and practical real-world scenarios. We show that a robust representation can be derived through a so-called causal front-door adjustment, based on a $\textit{decomposition}$ assumption, using fine-tuned representations as a source of data augmentation. Comprehensive experiments in both synthetic and real-world settings demonstrate the superior generalizability of the proposed method compared to existing approaches. Our work thus sheds light on the domain generalization problem by introducing links between fine-tuning and causal mechanisms into representation learning.

Primary Area: unsupervised, self-supervised, semi-supervised, and supervised representation learning

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Submission Number: 14004