Concept Bottleneck Large Language Models

• URL: http://arxiv.org/abs/2412.07992v1
• Date: Wed, 11 Dec 2024 00:04:10 GMT
• Title: Concept Bottleneck Large Language Models
• Authors: Chung-En Sun, Tuomas Oikarinen, Berk Ustun, Tsui-Wei Weng,
• Abstract summary: We introduce the Concept Bottleneck Large Language Model (CB-LLM) CB-LLM is a pioneering approach to creating inherently interpretable Large Language Models (LLMs) We show how interpretable neurons in CB-LLM can be used for concept detection and steering text generation.
• Score: 15.852686755743415
• License:
• Abstract: We introduce the Concept Bottleneck Large Language Model (CB-LLM), a pioneering approach to creating inherently interpretable Large Language Models (LLMs). Unlike traditional black-box LLMs that rely on post-hoc interpretation methods with limited neuron function insights, CB-LLM sets a new standard with its built-in interpretability, scalability, and ability to provide clear, accurate explanations. We investigate two essential tasks in the NLP domain: text classification and text generation. In text classification, CB-LLM narrows the performance gap with traditional black-box models and provides clear interpretability. In text generation, we show how interpretable neurons in CB-LLM can be used for concept detection and steering text generation. Our CB-LLMs enable greater interaction between humans and LLMs across a variety of tasks -- a feature notably absent in existing LLMs. Our code is available at https://github.com/Trustworthy-ML-Lab/CB-LLMs.

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