Feature-Level Insights into Artificial Text Detection with Sparse
  Autoencoders
Authors: name: Kristian Kuznetsov, name: Laida Kushnareva, name: Polina Druzhinina, name: Anton Razzhigaev, name: Anastasia Voznyuk, name: Irina Piontkovskaya, name: Evgeny Burnaev, name: Serguei Barannikov

Artificial Text Detection (ATD) is becoming increasingly important with the
rise of advanced Large Language Models (LLMs). Despite numerous efforts, no
single algorithm performs consistently well across different types of unseen
text or guarantees effective generalization to new LLMs. Interpretability plays
a crucial role in achieving this goal. In this study, we enhance ATD
interpretability by using Sparse Autoencoders (SAE) to extract features from
Gemma-2-2b residual stream. We identify both interpretable and efficient
features, analyzing their semantics and relevance through domain- and
model-specific statistics, a steering approach, and manual or LLM-based
interpretation. Our methods offer valuable insights into how texts from various
models differ from human-written content. We show that modern LLMs have a
distinct writing style, especially in information-dense domains, even though
they can produce human-like outputs with personalized prompts.