B-cos LM: Efficiently Transforming Pre-trained Language Models for Improved Explainability

• URL: http://arxiv.org/abs/2502.12992v2
• Date: Mon, 14 Jul 2025 13:11:13 GMT
• Title: B-cos LM: Efficiently Transforming Pre-trained Language Models for Improved Explainability
• Authors: Yifan Wang, Sukrut Rao, Ji-Ung Lee, Mayank Jobanputra, Vera Demberg,
• Abstract summary: We introduce B-cos language models (LMs) empowered for natural language processing (NLP) tasks.<n>Our approach directly transforms pre-trained language models into B-cos LMs by combining B-cos conversion and task fine-tuning.<n>Our automatic and human evaluation results demonstrate that B-cos LMs produce more faithful and human interpretable explanations than post-hoc methods.
• Score: 21.480463138209483
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Post-hoc explanation methods for black-box models often struggle with faithfulness and human interpretability due to the lack of explainability in current neural architectures. Meanwhile, B-cos networks have been introduced to improve model explainability by proposing an architecture that removes bias terms and promotes input-weight alignment. Although B-cos networks have shown success in building explainable systems, 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 language models (LMs) empowered for natural language processing (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 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 are also the first to explore the transformation of decoder-only models to B-cos LMs for generation tasks.

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