A Survey on Sparse Autoencoders: Interpreting the Internal Mechanisms of Large Language Models (2025)

Abstract: Large Language Models (LLMs) have transformed natural language processing, yet their internal mechanisms remain largely opaque. Recently, mechanistic interpretability has attracted significant attention from the research community as a means to understand the inner workings of LLMs. Among various mechanistic interpretability approaches, Sparse Autoencoders (SAEs) have emerged as a promising method due to their ability to disentangle the complex, superimposed features within LLMs into more interpretable components. This paper presents a comprehensive survey of SAEs for interpreting and understanding the internal workings of LLMs. Our major contributions include: (1) exploring the technical framework of SAEs, covering basic architecture, design improvements, and effective training strategies; (2) examining different approaches to explaining SAE features, categorized into input-based and output-based explanation methods; (3) discussing evaluation methods for assessing SAE performance, covering both structural and functional metrics; and (4) investigating real-world applications of SAEs in understanding and manipulating LLM behaviors.

## introduction large language models (llms), such as @xcite , claude-3.5 @xcite , @xcite , and @xcite , have emerged as powerful tools in natural language processing, demonstrating remarkable capabilities in tasks ranging from text generation to complex reasoning. however, their increasing size and complexity have created significant challenges in understanding their internal representations and decision-making processes. this "black box" nature of llms has sparked a growing interest in mechanistic interpretability @xcite @xcite , a field that aims to break down llms into understandable components and systematically analyze how these components interact to understand their behaviors. among the various approaches to interpreting llms, sparse autoencoders (saes) @xcite @xcite @xcite have emerged as a particularly promising direction for addressing a fundamental challenge in llm interpretability: polysemanticity. many neurons in llms are polysemantic, responding to seemingly unrelated concepts or features simultaneously. this is a phenomenon likely resulting from superposition @xcite , where llms represent more independent features than they have neurons by encoding each feature as a linear combination of neurons. saes address this issue by learning an overcomplete, sparse representation of neural activations, effectively disentangling these superimposed features into more interpretable units. by training a sparse autoencoder to reconstruct the activations of a target network layer while enforcing sparsity constraints, saes can extract a larger set of monosemantic features that offer clearer insights into what information the llm is processing. this approach has shown promise in transforming the often-inscrutable activations of llms into more human-understandable representations, potentially creating a more effective vocabulary for mechanistic analysis of these complex systems. ## explainability analysis of saes this section aims to interpret the learned feature vectors from a trained sae with natural language. specifically, given a pre-defined vocabulary set v, the goal of the explainability analysis is to extract a subset of words i m ⊂ v to represent the meaning of w @xmath0, for @xmath1, ..., m . humans can understand the meaning of w m by reading their natural language explanations i m . there are two lines of work for this purpose, namely the input-based and output-based methods. figure 2 visualizes generated explanations of using different methods to interpret a learned feature vector. ## evaluation metrics and methods evaluating saes is inherently challenging due to the absence of ground truth labels. unlike traditional machine learning tasks where performance can be directly measured against labeled data, the quality of an sae must be inferred through a diverse set of metrics. these metrics assess both the internal structure of the model and its functional utility. to provide a comprehensive evaluation framework, we categorize sae evaluation methods into two main groups: structural metrics and functional metrics. this categorization ensures a holistic assessment of saes, covering both their training behavior and real-world applicability. ## applications in large language models the latents learned by saes represent a collection of low-level concepts. each sae latent can be interpreted through gathering its activating exam-ples @xcite . this approach enables latents to be interpreted in a human-understandable manner, thereby enhancing our comprehension of how models perform tasks and facilitating more effective control of model behaviors. ## conclusions in this survey, we provided a comprehensive examination of saes as a promising approach to interpreting and understanding llms. saes effectively address the challenge of polysemanticity through learning overcomplete, sparse representations that disentangle superimposed features into more interpretable units. we have systematically explored the foundational principles, technical frameworks, evaluation methodologies, and real-world applications of saes in the context of llm analysis. while saes have demonstrated considerable success in revealing the internal mechanisms of llms, several challenges remain. in appendix g, we discussed detailed research challenges, including the incompleteness of concept dictionaries, limited theoretical foundations, persistent reconstruction errors, and substantial computational requirements. despite these challenges, saes continue to evolve through architectural innovations and improved training strategies, offering deeper insights into the inner workings of complex llms.