The opaque nature of Large Language Models (LLMs) has led to significant research efforts aimed at enhancing their interpretability, primarily through post-hoc methods. More recent in-hoc approaches, such as Concept Bottleneck Models (CBMs), offer both interpretability and intervenability by incorporating explicit concept representations. However, these methods suffer from key limitations, including reliance on labeled concept datasets and significant architectural modifications that challenges re-integration into existing system pipelines. In this work, we introduce a new methodology for incorporating interpretability and intervenability into an existing model by integrating Concept Layers (CLs) into its architecture. Our approach projects the model's internal vector representations into a conceptual, explainable vector space before reconstructing and feeding them back into the model. Furthermore, we eliminate the need for a human-selected concept set by algorithmically searching an ontology for a set of concepts that can be either task-specific or task-agnostic. We evaluate CLs across multiple tasks, demonstrating that they maintain the original model's performance and agreement while enabling meaningful interventions. Additionally, we present a proof of concept showcasing an intervenability interface, allowing users to adjust model behavior dynamically, such as mitigating biases during inference.

本研究解决了大语言模型（LLMs）可解释性和可干预性不足的问题，提出了一种将概念层（CLs）集成到现有模型架构的方法。通过将模型内部向量表示投影到可解释的概念向量空间并进行重构，我们的方法无需人工选择概念集，并能在多任务中保持原模型性能，同时实现有效的干预。研究结果显示，该方法在动态调整模型行为方面具有良好潜力。 

概念层：通过大语言模型增强可解释性和可干预性