Prototypical Network-based Language Models (PNLMs) have been introduced as a novel approach for enhancing interpretability in deep learning models for NLP. In this work, we show that, despite the transparency afforded by their case-based reasoning architecture, current PNLMs are, in fact, not faithful, i.e. their explanations do not accurately reflect the underlying model’s reasoning process. By adopting an axiomatic approach grounded in the seminal works’ definition of faithfulness, we identify two specific points in the architecture of PNLMs where unfaithfulness may occur. To address this, we introduce Faithful Alignment (FA), a two-part framework that ensures the faithfulness of PNLMs’ explanations. We then demonstrate that FA achieves this goal without compromising model performance across a variety of downstream tasks and ablation studies.

Warning: This paper contains content that is stereotypical and may be upsetting. This paper addresses the issue of demographic stereotypes present in Transformer-based pre-trained language models (PLMs) and aims to deepen our understanding of how these biases are encoded in these models. To accomplish this, we introduce an easy-to-use framework for examining the stereotype-encoding behavior of PLMs through a combination of model probing and textual analyses. Our findings reveal that a small subset of attention heads within PLMs are primarily responsible for encoding stereotypes and that stereotypes toward specific minority groups can be identified using attention maps on these attention heads. Leveraging these insights, we propose an attention-head pruning method as a viable approach for debiasing PLMs, without compromising their language modeling capabilities or adversely affecting their performance on downstream tasks.