Title: Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit
Primary area: interpretability and explainable AI
Keywords: Data Analysis, Sparse Autoencoder, Embeddings, Model Evaluations

Abstract: Analyzing large-scale text corpora is a fundamental challenge in machine learning, crucial for tasks like auditing training data for biases or identifying undesirable model behaviors. Current methods often rely on costly LLM-based techniques (e.g. annotating dataset differences) or dense embedding models (e.g. for clustering), which lack control over the properties of interest. We propose using sparse autoencoders (SAEs) to create *SAE embeddings*: interpretable representations whose dimensions each map to a specific concept. Through four text analysis tasks, we show that SAE embeddings offer the controllability that dense embeddings lack while being far cheaper than LLMs. We show that SAE embeddings find useful insights in large amounts of text data, enabling undirected insight discovery, such as (1) identifying differences between datasets and (2) uncovering unexpected correlations between concepts. For example, by comparing model responses, we find that Grok-4 clarifies ambiguities more often than nine other frontier models. Relative to LLMs, SAE embeddings find more prominent differences at 2-8× lower cost and reveal correlations more reliably. Additionally, SAE embeddings are controllable: compared with dense embeddings, SAEs can (3) cluster texts into novel groupings and (4) query documents based on implicit properties. Lastly, we illustrate SAE embeddings' utility for understanding model behavior through two case studies: investigating how OpenAI model behavior has changed over new generations and uncovering a spurious correlation in Tulu-3's training data. These results position SAEs as a versatile tool for text data analysis and highlight the neglected importance of interpreting models through their *data*.