Concept Bottleneck Large Language Models

• URL: http://arxiv.org/abs/2412.07992v3
• Date: Thu, 03 Apr 2025 00:27:39 GMT
• Title: Concept Bottleneck Large Language Models
• Authors: Chung-En Sun, Tuomas Oikarinen, Berk Ustun, Tsui-Wei Weng,
• Abstract summary: CB-LLMs is a framework for building inherently interpretable Large Language Models.<n>We build CB-LLMs for two essential NLP tasks: text classification and text generation.<n> embedded interpretability empowers users to transparently identify harmful content, steer model behavior, and unlearn undesired concepts.
• Score: 15.852686755743415
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce Concept Bottleneck Large Language Models (CB-LLMs), a novel framework for building inherently interpretable Large Language Models (LLMs). In contrast to traditional black-box LLMs that rely on limited post-hoc interpretations, CB-LLMs integrate intrinsic interpretability directly into the LLMs -- allowing accurate explanations with scalability and transparency. We build CB-LLMs for two essential NLP tasks: text classification and text generation. In text classification, CB-LLMs is competitive with, and at times outperforms, traditional black-box models while providing explicit and interpretable reasoning. For the more challenging task of text generation, interpretable neurons in CB-LLMs enable precise concept detection, controlled generation, and safer outputs. The embedded interpretability empowers users to transparently identify harmful content, steer model behavior, and unlearn undesired concepts -- significantly enhancing the safety, reliability, and trustworthiness of LLMs, which are critical capabilities notably absent in existing models. Our code is available at https://github.com/Trustworthy-ML-Lab/CB-LLMs.

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