ICLR 2025

Published as a conference paper at ICLR 2025
MONET: MIXTURE OF MONOSEMANTIC EXPERTS FOR
TRANSFORMERS
Jungwoo Park1,3†, Young Jin Ahn2†, Kee-Eung Kim2⋆, Jaewoo Kang1,3⋆
1Korea University, 2KAIST, 3AIGEN Sciences
{jungwoo-park, kangj}@korea.ac.kr
{snoop2head, kekim}@kaist.ac.kr
ABSTRACT
Understanding the internal computations of large language models (LLMs) is
crucial for aligning them with human values and preventing undesirable behav-
iors like toxic content generation. However, mechanistic interpretability is hin-
dered by polysemanticity—where individual neurons respond to multiple, unre-
lated concepts.
While Sparse Autoencoders (SAEs) have attempted to disen-
tangle these features through sparse dictionary learning, they have compromised
LLM performance due to reliance on post-hoc reconstruction loss. To address
this issue, we introduce MIXTURE OF MONOSEMANTIC EXPERTS FOR TRANS-
FORMERS (MONET) architecture, which incorporates sparse dictionary learning
directly into end-to-end Mixture-of-Experts pretraining. Our novel expert decom-
position method enables scaling the expert count to 262,144 per layer while total
parameters scale proportionally to the square root of the number of experts. Our
analyses demonstrate mutual exclusivity of knowledge across experts and show-
case the parametric knowledge encapsulated within individual experts. Moreover,
MONET allows knowledge manipulation over domains, languages, and toxicity
mitigation without degrading general performance. Our pursuit of transparent
LLMs highlights the potential of scaling expert counts to enhance mechanistic
interpretability and directly resect the internal knowledge to fundamentally ad-
just model behavior. The source code and pretrained checkpoints are available at
https://github.com/dmis-lab/Monet.
1
INTRODUCTION
As large language models (LLMs) continue to scale and generalize (Radford et al., 2019; Brown
et al., 2020), understanding their internal computations becomes increasingly imperative. M...