CLIF: Concept-Level Influence Functions for Transparent Bottleneck Models
Authors: Yike Sun, Mingkun Xu, Mu You, Zhongzhi He, Henghua Shen, Zehan Tan, Derek F. Wong, Tao Fang
Organizations: Tandon School of Engineering, New York University, USA · Guangdong Institute of Intelligence Science and Technology, Hengqin, China · Macau Millennium College, Macau SAR, China · NLP2CT Lab, Department of Computer and Information Science, University of Macau, Macau SAR, China
In recent years, the black-box nature of deep learning models has limited their application in high-stakes domains such as medical diagnosis and finance, where interpretability is essential. To address this, we propose a novel approach using influence functions to enhance interpretability in NLP models at both the sample and concept levels. Experiments on CEBaB and Yelp datasets show that influence functions effectively identify the most impactful training samples, both helpful and harmful, on model predictions. By adjusting the labels and weights of these samples, we demonstrate that model performance can be restored to baseline levels without retraining, confirming the value of influence functions for efficient data debugging. Furthermore, our concept-level analysis identifies key concepts within Concept Bottleneck Models (CBM) that significantly affect predictions. Modifying these concepts alters model behavior observably, providing clear insights into the decision process.