title: ExaGPT: Example-Based Machine-Generated Text Detection for Human
    Interpretability
summary: Detecting texts generated by Large Language Models (LLMs) could cause grave
  mistakes due to incorrect decisions, such as undermining student's academic
  dignity. LLM text detection thus needs to ensure the interpretability of the
  decision, which can help users judge how reliably correct its prediction is.
  When humans verify whether a text is human-written or LLM-generated, they
  intuitively investigate with which of them it shares more similar spans.
  However, existing interpretable detectors are not aligned with the human
  decision-making process and fail to offer evidence that users easily
  understand. To bridge this gap, we introduce ExaGPT, an interpretable detection
  approach grounded in the human decision-making process for verifying the origin
  of a text. ExaGPT identifies a text by checking whether it shares more similar
  spans with human-written vs. with LLM-generated texts from a datastore. This
  approach can provide similar span examples that contribute to the decision for
  each span in the text as evidence. Our human evaluation demonstrates that
  providing similar span examples contributes more effectively to judging the
  correctness of the decision than existing interpretable methods. Moreover,
  extensive experiments in four domains and three generators show that ExaGPT
  massively outperforms prior powerful detectors by up to +40.9 points of
  accuracy at a false positive rate of 1%.
paper_authors._id: 67b52de36007d463b988b202
paper_authors.name: Ryuto Koike
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-19T09:03:41.013Z
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paper_authors.fullname: Ryuto Koike
paper_authors.type: user
paper_authors.user: ryuryukke
paper_authors._id: 67b52de36007d463b988b203
paper_authors.name: Masahiro Kaneko
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-19T15:46:06.323Z
paper_authors._id: 652bdb25756a15d750071787
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/652bdb25756a15d750071787/qWjkU0_1egP7XYX3sXoSZ.jpeg
paper_authors.fullname: Masahiro Kaneko
paper_authors.type: user
paper_authors.user: MasahiroKaneko
paper_authors._id: 67b52de36007d463b988b204
paper_authors.name: Ayana Niwa
paper_authors._id: 67b52de36007d463b988b205
paper_authors.name: Preslav Nakov
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-19T15:46:29.303Z
paper_authors._id: 647f7eb25e1bc4753746bf9f
paper_authors.avatarUrl: /avatars/cc9c6210fdc822d8a106937e747dff41.svg
paper_authors.fullname: Preslav Nakov
paper_authors.type: user
paper_authors.user: preslavnakov
paper_authors._id: 67b52de36007d463b988b206
paper_authors.name: Naoaki Okazaki
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-19T15:46:36.241Z
paper_authors._id: 630c0a9b09eceb8fafe89cc6
paper_authors.avatarUrl: /avatars/6325a9b34da54d5cbddb814c3987a2fe.svg
paper_authors.fullname: Naoaki Okazaki
paper_authors.type: user
paper_authors.user: nokazaki
paper_title: ExaGPT: Example-Based Machine-Generated Text Detection for Human
    Interpretability
paper_summary: Detecting texts generated by Large Language Models (LLMs) could cause grave
  mistakes due to incorrect decisions, such as undermining student's academic
  dignity. LLM text detection thus needs to ensure the interpretability of the
  decision, which can help users judge how reliably correct its prediction is.
  When humans verify whether a text is human-written or LLM-generated, they
  intuitively investigate with which of them it shares more similar spans.
  However, existing interpretable detectors are not aligned with the human
  decision-making process and fail to offer evidence that users easily
  understand. To bridge this gap, we introduce ExaGPT, an interpretable detection
  approach grounded in the human decision-making process for verifying the origin
  of a text. ExaGPT identifies a text by checking whether it shares more similar
  spans with human-written vs. with LLM-generated texts from a datastore. This
  approach can provide similar span examples that contribute to the decision for
  each span in the text as evidence. Our human evaluation demonstrates that
  providing similar span examples contributes more effectively to judging the
  correctness of the decision than existing interpretable methods. Moreover,
  extensive experiments in four domains and three generators show that ExaGPT
  massively outperforms prior powerful detectors by up to +40.9 points of
  accuracy at a false positive rate of 1%.
paper_ai_keywords: Large Language Models (LLMs) interpretable detectors human decision-making similar spans datastore human evaluation false positive rate