title: Feature-Level Insights into Artificial Text Detection with Sparse
    Autoencoders
summary: 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.
paper_authors._id: 67cbfff12cc05acaab147f07
paper_authors.name: Kristian Kuznetsov
paper_authors._id: 67cbfff12cc05acaab147f08
paper_authors.name: Laida Kushnareva
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-11T08:23:18.630Z
paper_authors._id: 636254dc2691058b19d9276a
paper_authors.avatarUrl: /avatars/36eb0e27e0e321fb0ac513f0d4d67c95.svg
paper_authors.fullname: Kushnareva
paper_authors.type: user
paper_authors.user: Kushnareva
paper_authors._id: 67cbfff12cc05acaab147f09
paper_authors.name: Polina Druzhinina
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-11T10:23:35.842Z
paper_authors._id: 65d5e094cd05bc1eaa0fafc9
paper_authors.avatarUrl: /avatars/ea3d52def6ef4d9af07728a76a499a9f.svg
paper_authors.fullname: Polina Druzhinina
paper_authors.type: user
paper_authors.user: plina2polina
paper_authors._id: 67cbfff12cc05acaab147f0a
paper_authors.name: Anton Razzhigaev
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-11T08:23:21.197Z
paper_authors._id: 6172aaeec8e66e2aa84c06b9
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paper_authors.fullname: Anton Razzhigaev
paper_authors.type: user
paper_authors.user: razzant
paper_authors._id: 67cbfff12cc05acaab147f0b
paper_authors.name: Anastasia Voznyuk
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-11T10:23:50.503Z
paper_authors._id: 64cbfc39a81988d0735867a1
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/64cbfc39a81988d0735867a1/JLlYTaRa70H4MzLXOHhEB.jpeg
paper_authors.fullname: Anastasia Voznyuk
paper_authors.type: user
paper_authors.user: natriistorm
paper_authors._id: 67cbfff12cc05acaab147f0c
paper_authors.name: Irina Piontkovskaya
paper_authors._id: 67cbfff12cc05acaab147f0d
paper_authors.name: Evgeny Burnaev
paper_authors._id: 67cbfff12cc05acaab147f0e
paper_authors.name: Serguei Barannikov
paper_title: Feature-Level Insights into Artificial Text Detection with Sparse
    Autoencoders
paper_summary: 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.
paper_ai_keywords: Sparse Autoencoders Gemma-2-2b residual stream interpretability domain-specific statistics model-specific statistics steering approach LLM-based interpretation writing style information-dense domains human-like outputs