CB-LLMs introduce a framework that builds interpretability directly into language models, rather than relying on after-the-fact explanations, improving transparency and trustworthiness.

• Creates models that explain why they make predictions using meaningful concepts
• Improves safety and trust by allowing visibility into model reasoning
• Achieves strong performance on both text classification and generation tasks
• Enables identification of harmful content through transparent reasoning paths

This research matters for security professionals because it offers a path to AI systems that can be audited, understood, and verified—essential requirements for high-risk applications where unexplainable decisions are unacceptable.

Concept Bottleneck Large Language Models