--- date: '2023-01-29T10:35:56' hypothesis-meta: created: '2023-01-29T10:35:56.649264+00:00' document: title: - 2301.11305.pdf flagged: false group: __world__ hidden: false id: tr0lTp_AEe2k81d5ilJ0Xw links: html: https://hypothes.is/a/tr0lTp_AEe2k81d5ilJ0Xw incontext: https://hyp.is/tr0lTp_AEe2k81d5ilJ0Xw/arxiv.org/pdf/2301.11305.pdf json: https://hypothes.is/api/annotations/tr0lTp_AEe2k81d5ilJ0Xw permissions: admin: - acct:ravenscroftj@hypothes.is delete: - acct:ravenscroftj@hypothes.is read: - group:__world__ update: - acct:ravenscroftj@hypothes.is tags: - chatgpt - detecting gpt target: - selector: - end: 1096 start: 756 type: TextPositionSelector - exact: 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) prefix: ' is generated from a givenLLM. T' suffix: . We find DetectGPT is more disc type: TextQuoteSelector source: https://arxiv.org/pdf/2301.11305.pdf text: 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. updated: '2023-01-29T10:35:56.649264+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/2023/01/29/1674988556 ---
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)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.