61 lines
2.1 KiB
Markdown
61 lines
2.1 KiB
Markdown
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---
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date: '2023-01-29T10:57:24'
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hypothesis-meta:
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created: '2023-01-29T10:57:24.658922+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: tnNraJ_DEe2YBceDAVt0Uw
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links:
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html: https://hypothes.is/a/tnNraJ_DEe2YBceDAVt0Uw
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incontext: https://hyp.is/tnNraJ_DEe2YBceDAVt0Uw/arxiv.org/pdf/2301.11305.pdf
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json: https://hypothes.is/api/annotations/tnNraJ_DEe2YBceDAVt0Uw
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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: 22349
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start: 22098
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type: TextPositionSelector
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- exact: "Empirically, we find predictive entropy to be positively cor-related\
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\ with passage fake-ness more often that not; there-fore, this baseline uses\
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\ high average entropy in the model\u2019spredictive distribution as a signal\
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\ that a passage is machine-generated."
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prefix: tropy) predictive distributions.
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suffix: ' While our main focus is on zero'
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type: TextQuoteSelector
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source: https://arxiv.org/pdf/2301.11305.pdf
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text: this makes sense and aligns with the [gltr](http://gltr.io) - humans add more
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entropy to sentences by making unusual choices in vocabulary that a model would
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not.
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updated: '2023-01-29T10:57:24.658922+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/2023/01/29/1674989844
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---
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<blockquote>Empirically, we find predictive entropy to be positively cor-related with passage fake-ness more often that not; there-fore, this baseline uses high average entropy in the model’spredictive distribution as a signal that a passage is machine-generated.</blockquote>this makes sense and aligns with the [gltr](http://gltr.io) - humans add more entropy to sentences by making unusual choices in vocabulary that a model would not.
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