Sukrut Rao (PhD Student)

Post-hoc explanation methods for black-box models often struggle with
faithfulness and human interpretability due to the lack of explainability in
current neural models. Meanwhile, B-cos networks have been introduced to
improve model explainability through architectural and computational
adaptations, but their application has so far been limited to computer vision
models and their associated training pipelines. In this work, we introduce
B-cos LMs, i.e., B-cos networks empowered for NLP tasks. Our approach directly
transforms pre-trained language models into B-cos LMs by combining B-cos
conversion and task fine-tuning, improving efficiency compared to previous
B-cos methods. Our automatic and human evaluation results demonstrate that
B-cos LMs produce more faithful and human interpretable explanations than post
hoc methods, while maintaining task performance comparable to conventional
fine-tuning. Our in-depth analysis explores how B-cos LMs differ from
conventionally fine-tuned models in their learning processes and explanation
patterns. Finally, we provide practical guidelines for effectively building
B-cos LMs based on our findings. Our code is available at
anonymous.4open.science/r/bcos_lm.

B-cos Networks have been shown to be effective for obtaining highly human
interpretable explanations of model decisions by architecturally enforcing
stronger alignment between inputs and weight. B-cos variants of convolutional
networks (CNNs) and vision transformers (ViTs), which primarily replace linear
layers with B-cos transformations, perform competitively to their respective
standard variants while also yielding explanations that are faithful by design.
However, it has so far been necessary to train these models from scratch, which
is increasingly infeasible in the era of large, pre-trained foundation models.
In this work, inspired by the architectural similarities in standard DNNs and
B-cos networks, we propose 'B-cosification', a novel approach to transform
existing pre-trained models to become inherently interpretable. We perform a
thorough study of design choices to perform this conversion, both for
convolutional neural networks and vision transformers. We find that
B-cosification can yield models that are on par with B-cos models trained from
scratch in terms of interpretability, while often outperforming them in terms
of classification performance at a fraction of the training cost. Subsequently,
we apply B-cosification to a pretrained CLIP model, and show that, even with
limited data and compute cost, we obtain a B-cosified version that is highly
interpretable and competitive on zero shot performance across a variety of
datasets. We release our code and pre-trained model weights at
github.com/shrebox/B-cosification.