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---
date: '2023-01-29T11:01:22'
hypothesis-meta:
created: '2023-01-29T11:01:22.509728+00:00'
document:
title:
- 2301.11305.pdf
flagged: false
group: __world__
hidden: false
id: RDbfNJ_EEe258oPTYQZGNA
links:
html: https://hypothes.is/a/RDbfNJ_EEe258oPTYQZGNA
incontext: https://hyp.is/RDbfNJ_EEe258oPTYQZGNA/arxiv.org/pdf/2301.11305.pdf
json: https://hypothes.is/api/annotations/RDbfNJ_EEe258oPTYQZGNA
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: 11561
start: 11236
type: TextPositionSelector
- exact: "As in prior work, we study a \u2018white box\u2019 setting (Gehrmannet\
\ al., 2019) in which the detector may evaluate the log prob-ability of a\
\ sample log p\u03B8 (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"
prefix: ed samples to perform detection.
suffix: . While most of our ex-periments
type: TextQuoteSelector
source: https://arxiv.org/pdf/2301.11305.pdf
text: 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.
updated: '2023-01-29T11:01:22.509728+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/2023/01/29/1674990082
---
<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.