arXiv：cs.CL· Hasan Amin, Zhuoran Lu, Ming Yin·· 16 小时前
研究揭示：基于似然的AI辅助写作人类贡献度测量可被轻易操纵
Likelihood Is Not Authorship: Can We Reliably Measure Human Contribution in AI-Assisted Writing?
AI 导读
一项 arXiv 研究提出 SPECIFICITY PADDING 方法，仅让模型复制提示词中的几个随机 ID，就能让纯标题请求看起来比提供核心思想的请求更像人类写作。
正文
Abstract:AI-text detection generally asks whether a machine wrote a text. As AI becomes an increasingly integral and accepted part of writing, the more relevant question is no longer whether AI was used, but how much of the text reflects human contribution. A prominent line of work estimates this by measuring how predictive a person's prompt is of the final text. We show that this measure can be easily manipulated. We introduce SPECIFICITY PADDING that simply asks the model to copy a few random IDs from the prompt, and that alone makes a title-only request look more human than one that supplies the article's key ideas. A context-swap audit then holds each document fixed, and only shows or hides the IDs to the evaluator. The score of the unchanged text swings by a third to half of its scale, and it rises for every document we test, on news and scientific abstracts, across varied LLMs from 3.8B to 753B parameters. The same articles can average 85\% human or 15\% human depending only on which prompt is on record, and even with no prompt at all, the output-only variant rewards the random strings themselves. An exact identity, the credit ledger, explains why. The score is a surprisal-weighted average over the text, so rare strings that the context makes copyable collect the credit, whatever they mean. A two-point bound marks a deeper limit. If the prompt could have been machine-written, every transcript-only method must, in the worst case, miss by half the contribution at stake. Likelihood-based scores alone cannot reliably establish human contribution without evidence of what the person added and how the text was produced.
| Subjects: | Computation and Language (cs.CL) |
| ACM classes: | I.2.7 |
| Cite as: | arXiv:2610.19767 [cs.CL] |
| (or arXiv:2610.19767v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.19767 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Zhuoran Lu [view email]
[v1]
Wed, 14 Oct 2026 08:18:02 UTC (326 KB)
来源：arXiv：cs.CL · arxiv.org