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brainsteam/content/annotations/2023/01/29/1674988556.md

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+---
+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
+
+---
+
+
+
+ <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.