Bridging the Faithfulness Gap in Prototypical Models (2025)

Abstract: 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.

## introduction in recent years, deep learning-based language models have drastically enhanced performance across various nlp tasks @xcite . despite their high predictive accuracy, these models remain opaque, meaning their decision-making processes are not easily understandable to humans. consequently, numerous explainable ai (xai) techniques have been developed to interpret model decisions for end users @xcite @xcite . a recent advancement in the xai domain is the use of prototypical networks for interpretability. originally introduced for few-shot learning @xcite , prototypical networks offer a unique advantage in interpretability due to their case-based reasoning architecture. although initially adapted for increased interpretability in computer vision (cv) tasks @xcite @xcite , several recent works in nlp have begun to develop similar prototypical-based models to enhance interpretability in nlp contexts @xcite @xcite . despite their application in a wide range of tasks from propaganda detection @xcite to icd-9 diagnosis prediction @xcite , the faithfulness foot_0 of pnlms remains unexamined. faithfulness is a necessary condition for any deployed machine learning model since unfaithful model explanations can lead to dangerous outcomes such as leading a user to trust a model's incorrect prediction simply because its explanation looks convincing @xcite @xcite . in this work, we assess the faithfulness of pnlms using axioms from seminal interpretability studies @xcite and identify two key flaws in state-of-the-art pnlm architectures @xcite @xcite that result in explanations of current pnlms to be unfaithful (see figure 1 ). to address these shortcomings, we propose faithful alignment, a two-part framework designed to ensure faithful prototypical model explanations. our contributions are as follows: ## unfaithfulness in prototypical networks we briefly outline the pnlm workflow before describing two architectural shortcomings that lead to unfaithfulness. let f θ be a language model (e.g., bert) such that f θ : r l×@xmath0, a sequence of l embedded tokens, to a compressed hidden representation f θ (x) ∈ r h . through specialized training objectives, the pnlm learns @xmath1, a set of m prototypes where p @xmath2. during inference, the pnlm feeds f θ (x) into the prototypical space to obtain @xmath3. these similarities in m are then either passed through a final linear layer w @xmath4. this architecture enables pnlms to generate textual explanations by ## solution: faithful alignment in this section we describe faithful alignment (fa), a two-part solution consisting of faithful retrieval (fr) and faithful projection (fp). and the learned prototypes p j influences the model's prediction, yet the model's explanations for p j are derived from s i which does not participate in the computation of the output. (right) faithfulness gap evaluated for proto-lm and prototex on two datasets (sst5 and hoc). for each learned prototype p j , we compute the l 2 distance to the encoding of its assigned textual representation. the green box indicates prototype representations that would be perfectly faithful i.e. 0 faithfulness gap. the red box highlights the fact that there exists a faithfulness gap for all prototypes in both models, empirically demonstrating the misalignment between model reasoning and model explanations. ## effect of fa on downstream tasks because fa makes pnlm architectures mechanically faithful, it is important to examine its impact on model performance. specifically, we conduct experiments to compare the performance of pnlms without fa against fa-applied pnlms. in ## conclusion in this work, we build on foundational ideas in xai, interpretability, and faithfulness to identify two key shortcomings of existing pnlms that cause their explanations to be unfaithful: 1) the selection of incorrect prototypes for explanation, and 2) the misrepresentation of prototypes in model reasoning. to address these issues, we introduce a faithfulness-ensuring framework, fa, and validate its robustness through extensive experiments. we believe our contribution bridges a crucial gap in the current understanding of faithfulness in the context of prototypical models. we hope future prototypical networks can leverage our framework to prevent unfaithful explanations.