Title: ProtSAE: Disentangling and Interpreting Protein Language Models via Semantically-Guided Sparse Autoencoders Show Chinese title

Title: ProtSAE：通过语义引导的稀疏自编码器解耦和解释蛋白质语言模型

Abstract: Sparse Autoencoder (SAE) has emerged as a powerful tool for mechanistic interpretability of large language models. Recent works apply SAE to protein language models (PLMs), aiming to extract and analyze biologically meaningful features from their latent spaces. However, SAE suffers from semantic entanglement, where individual neurons often mix multiple nonlinear concepts, making it difficult to reliably interpret or manipulate model behaviors. In this paper, we propose a semantically-guided SAE, called ProtSAE. Unlike existing SAE which requires annotation datasets to filter and interpret activations, we guide semantic disentanglement during training using both annotation datasets and domain knowledge to mitigate the effects of entangled attributes. We design interpretability experiments showing that ProtSAE learns more biologically relevant and interpretable hidden features compared to previous methods. Performance analyses further demonstrate that ProtSAE maintains high reconstruction fidelity while achieving better results in interpretable probing. We also show the potential of ProtSAE in steering PLMs for downstream generation tasks. Show Chinese abstract

Abstract: 稀疏自编码器（SAE）已成为解释大型语言模型机制的重要工具。 近期的研究将SAE应用于蛋白质语言模型（PLMs），旨在从其潜在空间中提取和分析生物学上有意义的特征。 然而，SAE存在语义纠缠问题，其中单个神经元通常混合多个非线性概念，使得可靠地解释或操作模型行为变得困难。 在本文中，我们提出了一种语义引导的SAE，称为ProtSAE。 与现有SAE需要标注数据集来过滤和解释激活不同，我们在训练过程中使用标注数据集和领域知识来指导语义解缠，以减轻纠缠属性的影响。 我们设计了可解释性实验，结果表明，与之前的方法相比，ProtSAE学习到的隐藏特征更具生物学相关性和可解释性。 性能分析进一步表明，ProtSAE在保持高重建保真度的同时，在可解释性探测中取得了更好的结果。 我们还展示了ProtSAE在引导PLMs进行下游生成任务中的潜力。