61 lines
2.2 KiB
Markdown
61 lines
2.2 KiB
Markdown
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---
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date: '2023-01-29T10:35:56'
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hypothesis-meta:
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created: '2023-01-29T10:35:56.649264+00:00'
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document:
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title:
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- 2301.11305.pdf
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flagged: false
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group: __world__
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hidden: false
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id: tr0lTp_AEe2k81d5ilJ0Xw
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links:
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html: https://hypothes.is/a/tr0lTp_AEe2k81d5ilJ0Xw
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incontext: https://hyp.is/tr0lTp_AEe2k81d5ilJ0Xw/arxiv.org/pdf/2301.11305.pdf
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json: https://hypothes.is/api/annotations/tr0lTp_AEe2k81d5ilJ0Xw
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permissions:
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admin:
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- acct:ravenscroftj@hypothes.is
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delete:
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- acct:ravenscroftj@hypothes.is
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read:
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- group:__world__
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update:
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- acct:ravenscroftj@hypothes.is
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tags:
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- chatgpt
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- detecting gpt
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target:
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- selector:
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- end: 1096
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start: 756
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type: TextPositionSelector
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- exact: his approach, which we call DetectGPT,does not require training a separate
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classifier, col-lecting a dataset of real or generated passages, orexplicitly
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watermarking generated text. It usesonly log probabilities computed by the
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model ofinterest and random perturbations of the passagefrom another generic
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pre-trained language model(e.g, T5)
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prefix: ' is generated from a givenLLM. T'
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suffix: . We find DetectGPT is more disc
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type: TextQuoteSelector
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source: https://arxiv.org/pdf/2301.11305.pdf
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text: The novelty of this approach is that it is cheap to set up as long as you
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have the log probabilities generated by the model of interest.
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updated: '2023-01-29T10:35:56.649264+00:00'
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uri: https://arxiv.org/pdf/2301.11305.pdf
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user: acct:ravenscroftj@hypothes.is
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user_info:
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display_name: James Ravenscroft
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in-reply-to: https://arxiv.org/pdf/2301.11305.pdf
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tags:
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- chatgpt
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- detecting gpt
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- hypothesis
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type: annotation
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url: /annotations/2023/01/29/1674988556
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---
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<blockquote>his approach, which we call DetectGPT,does not require training a separate classifier, col-lecting a dataset of real or generated passages, orexplicitly watermarking generated text. It usesonly log probabilities computed by the model ofinterest and random perturbations of the passagefrom another generic pre-trained language model(e.g, T5)</blockquote>The novelty of this approach is that it is cheap to set up as long as you have the log probabilities generated by the model of interest.
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