ICML 2025

SAE-V: Interpreting Multimodal Models for Enhanced Alignment
Hantao Lou * 1 2 Changye Li * 1 2 Jiaming Ji 1 2 Yaodong Yang 1 2
Abstract
With the integration of image modality, the seman-
tic space of multimodal large language models
(MLLMs) is more complex than text-only mod-
els, making their interpretability more challeng-
ing and their alignment less stable, particularly
susceptible to low-quality data, which can lead
to inconsistencies between modalities, hallucina-
tions, and biased outputs. As a result, developing
interpretability methods for MLLMs is crucial
for improving alignment quality and efficiency.
In text-only LLMs, Sparse Autoencoders (SAEs)
have gained attention for their ability to interpret
latent representations. However, extending SAEs
to multimodal settings presents new challenges
due to modality fusion and the difficulty of isolat-
ing cross-modal representations. To address these
challenges, we introduce SAE-V, a mechanistic
interpretability framework that extends the SAE
paradigm to MLLMs. By identifying and analyz-
ing interpretable features along with their corre-
sponding data, SAE-V enables fine-grained inter-
pretation of both model behavior and data quality,
facilitating a deeper understanding of cross-modal
interactions and alignment dynamics. Moreover,
by utilizing cross-modal feature weighting, SAE-
V provides an intrinsic data filtering mechanism
to enhance model alignment without requiring ad-
ditional models. Specifically, when applied to the
alignment process of MLLMs, SAE-V-based data
filtering methods could achieve more than 110%
performance with less than 50% data. Our results
highlight SAE-V’s ability to enhance interpretabil-
ity and alignment in MLLMs, providing insights
into their internal mechanisms. *
*Equal contribution 1Institute for AI, Peking University, Beijing,
China 2State Key Laboratory of General Artificial Intelligence, In-
stitute for AI, Peking University, Beijing, China. Correspondence
to: Hantao Lou <hanta...