Sparsity-Guided Holistic Explanation for LLMs with Interpretable
  Inference-Time Intervention
Zhen Tan Tianlong Chen Zhenyu Zhang Huan Liu
Large Language Models (LLMs) have achieved unprecedented breakthroughs in
various natural language processing domains. However, the enigmatic
``black-box'' nature of LLMs remains a significant challenge for
interpretability, hampering transparent and accountable applications. While
past approaches, such as attention visualization, pivotal subnetwork
extraction, and concept-based analyses, offer some insight, they often focus on
either local or global explanations within a single dimension, occasionally
falling short in providing comprehensive clarity. In response, we propose a
novel methodology anchored in sparsity-guided techniques, aiming to provide a
holistic interpretation of LLMs. Our framework, termed SparseCBM, innovatively
integrates sparsity to elucidate three intertwined layers of interpretation:
input, subnetwork, and concept levels. In addition, the newly introduced
dimension of interpretable inference-time intervention facilitates dynamic
adjustments to the model during deployment. Through rigorous empirical
evaluations on real-world datasets, we demonstrate that SparseCBM delivers a
profound understanding of LLM behaviors, setting it apart in both interpreting
and ameliorating model inaccuracies. Codes are provided in supplements.