Feature-Level Insights into Artificial Text Detection with Sparse
  Autoencoders
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.
67cbfff12cc05acaab147f07
Kristian Kuznetsov
67cbfff12cc05acaab147f08
Laida Kushnareva
claimed_verified
2025-03-11T08:23:18.630Z
636254dc2691058b19d9276a
/avatars/36eb0e27e0e321fb0ac513f0d4d67c95.svg
Kushnareva
user
Kushnareva
67cbfff12cc05acaab147f09
Polina Druzhinina
admin_assigned
2025-03-11T10:23:35.842Z
65d5e094cd05bc1eaa0fafc9
/avatars/ea3d52def6ef4d9af07728a76a499a9f.svg
Polina Druzhinina
user
plina2polina
67cbfff12cc05acaab147f0a
Anton Razzhigaev
claimed_verified
2025-03-11T08:23:21.197Z
6172aaeec8e66e2aa84c06b9
https://cdn-avatars.huggingface.co/v1/production/uploads/6172aaeec8e66e2aa84c06b9/ZdRZSp3P1SU6CIDbvQwkv.jpeg
Anton Razzhigaev
user
razzant
67cbfff12cc05acaab147f0b
Anastasia Voznyuk
admin_assigned
2025-03-11T10:23:50.503Z
64cbfc39a81988d0735867a1
https://cdn-avatars.huggingface.co/v1/production/uploads/64cbfc39a81988d0735867a1/JLlYTaRa70H4MzLXOHhEB.jpeg
Anastasia Voznyuk
user
natriistorm
67cbfff12cc05acaab147f0c
Irina Piontkovskaya
67cbfff12cc05acaab147f0d
Evgeny Burnaev
67cbfff12cc05acaab147f0e
Serguei Barannikov
Feature-Level Insights into Artificial Text Detection with Sparse
  Autoencoders
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.
Sparse Autoencoders enhance interpretability in Artificial Text Detection by extracting distinctive features from LLM outputs, providing insights into differences from human-written texts.
Sparse Autoencoders residual stream interpretable features domain-specific statistics steering approach model-specific statistics