---
date: '2023-01-29T10:53:23'
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    title:
    - 2301.11305.pdf
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  id: JoZDmp_DEe2QSbcSZI0WEA
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  tags:
  - chatgpt
  - detecting gpt
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    - exact: We find that supervised detectors can provide similardetection performance
        to DetectGPT on in-distribution datalike English news, but perform significantly
        worse than zero-shot methods in the case of English scientific writing andfail
        altogether for German writing. T
      prefix: 'm each dataset for evalua-tion. '
      suffix: his finding echoes pastwork show
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    source: https://arxiv.org/pdf/2301.11305.pdf
  text: supervised detection methods fail on out of domain examples whereas detectgpt
    seems to be robust to changes in domain.
  updated: '2023-01-29T10:53:23.198884+00:00'
  uri: https://arxiv.org/pdf/2301.11305.pdf
  user: acct:ravenscroftj@hypothes.is
  user_info:
    display_name: James Ravenscroft
in-reply-to: https://arxiv.org/pdf/2301.11305.pdf
tags:
- chatgpt
- detecting gpt
- hypothesis
type: annotation
url: /annotations/2023/01/29/1674989603

---



 <blockquote>We find that supervised detectors can provide similardetection performance to DetectGPT on in-distribution datalike English news, but perform significantly worse than zero-shot methods in the case of English scientific writing andfail altogether for German writing. T</blockquote>supervised detection methods fail on out of domain examples whereas detectgpt seems to be robust to changes in domain.