Beyond Input Activations: Identifying Influential Latents by Gradient Sparse Autoencoders
Dong Shu, Xuansheng Wu, Haiyan Zhao, Mengnan Du, Ninghao Liu
Abstract
Sparse Autoencoders (SAEs) have recently emerged as powerful tools for interpreting and steering the internal representations of large language models (LLMs). However, conventional approaches to analyzing SAEs typically rely solely on input-side activations, without considering the influence between each latent feature and the model’s output. This work is built on two key hypotheses: (1) activated latents do not contribute equally to the construction of the model’s output, and (2) only latents with high influence are effective for model steering. To validate these hypotheses, we propose Gradient Sparse Autoencoder (GradSAE), a simple yet effective method that identifies the most influential latents by incorporating output-side gradient information.
BibTeX
@inproceedings{emnlp2025_beyondinputactiv,
title = {Beyond Input Activations: Identifying Influential Latents by Gradient Sparse Autoencoders},
author = {Dong Shu and Xuansheng Wu and Haiyan Zhao and Mengnan Du and Ninghao Liu},
booktitle = {EMNLP 2025},
year = {2025}
}