Deep Learning models encode rich semantic information in their hidden representations. However, it remains challenging to understand which parts of this information models actually rely on when making predictions. A promising line of post-hoc concept-based explanation methods relies on clustering token representations. However, commonly used approaches such as hierarchical clustering are computationally infeasible for large-scale datasets, and K-Means often yields shallow or frequency-dominated clusters. We propose the vector quantized latent concept (VQLC) method, a framework built upon the vector quantized-variational autoencoder (VQ-VAE) architecture that learns a discrete codebook mapping continuous representations to concept vectors. We perform thorough evaluations and show that VQLC improves scalability while maintaining comparable quality of human-understandable explanations.
@article{arxiv.2602.02726,
title = {Vector Quantized Latent Concepts: A Scalable Alternative to Clustering-Based Concept Discovery},
author = {Xuemin Yu and Ankur Garg and Samira Ebrahimi Kahou and Hassan Sajjad},
journal= {arXiv preprint arXiv:2602.02726},
year = {2026}
}