--- 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 ---
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 existThe 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.