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---
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date: '2023-01-29T11:01:22'
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hypothesis-meta:
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created: '2023-01-29T11:01:22.509728+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: RDbfNJ_EEe258oPTYQZGNA
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links:
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html: https://hypothes.is/a/RDbfNJ_EEe258oPTYQZGNA
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incontext: https://hyp.is/RDbfNJ_EEe258oPTYQZGNA/arxiv.org/pdf/2301.11305.pdf
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json: https://hypothes.is/api/annotations/RDbfNJ_EEe258oPTYQZGNA
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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: 11561
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start: 11236
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type: TextPositionSelector
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- exact: "As in prior work, we study a \u2018white box\u2019 setting (Gehrmannet\
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\ al., 2019) in which the detector may evaluate the log prob-ability of a\
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\ sample log p\u03B8 (x). The white box setting doesnot assume access to the\
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\ model architecture or parameters.While most public APIs for LLMs (such as\
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\ GPT-3) enablescoring text, some exceptions exist"
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prefix: ed samples to perform detection.
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suffix: . While most of our ex-periments
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type: TextQuoteSelector
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source: https://arxiv.org/pdf/2301.11305.pdf
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text: The authors assume white-box access to the log probability of a sample \(log
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p_{\Theta}(x)\) but do not require access to the model's actual architecture or
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weights.
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updated: '2023-01-29T11:01:22.509728+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/2023/01/29/1674990082
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---
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<blockquote>As in prior work, we study a ‘white box’ setting (Gehrmannet al., 2019) in which the detector may evaluate the log prob-ability of a sample log pθ (x). The white box setting doesnot assume access to the model architecture or parameters.While most public APIs for LLMs (such as GPT-3) enablescoring text, some exceptions exist</blockquote>The authors assume white-box access to the log probability of a sample \(log p_{\Theta}(x)\) but do not require access to the model's actual architecture or weights.
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