arXiv cs.AI· Alex Jiang, Didier Schwab, Chuyuan Li·· 1 天前AI 评分31
SPADE：用软提示检测 AI 生成文本
SPADE: Soft Prompts for Ai-text DEtection
AI 导读
SPADE 提出用软提示检测 AI 生成文本的新框架，通过引入 Pointwise Mutual Information（PMI）项衡量提示词对文本的解释程度，并以此作为判别信号。该方法为每个类别训练一个软提示以放大区分度，预测最能解释候选文本的提示所属类别。在多个基准测试中，SPADE 在分布外（OOD）数据集上取得 SOTA 性能。
正文 · 原文
Abstract:The easy access to Large Language Models (LLMs) has made AI-generated texts on the web increasingly ubiquitous, making AI-text detection a crucial task. Prompts greatly influence the generation process, but existing detectors focus solely on the generated texts --- supervised methods learn from labeled texts only, and zero-shot methods rely on the text's probability distributions --- ignoring the underlying generation process, which limits their robustness in open-world scenarios. We introduce SPADE: Soft Prompts for Ai-text DEtection, a novel framework for detecting AI-generated text that transfers better across datasets. Specifically, we measure the prompt's influence and use it as a discriminative signal. We characterize the prompt's influence by introducing a Pointwise Mutual Information (PMI) term that captures how much a prompt explains a text. Based on this quantity, SPADE trains one soft prompt per class to amplify the separation, and predicts the class whose prompt best explains the candidate text. Extensive experiments on several benchmarks against various detectors demonstrate the robustness and effectiveness of our method, achieving state-of-the-art (SOTA) performance on out-of-distribution (OOD) datasets.
| Comments: | 24 pages (8 main), 10 figures, 22 tables |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| ACM classes: | I.2.7 |
| Cite as: | arXiv:2611.16929 [cs.CL] |
| (or arXiv:2611.16929v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2611.16929 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Alex Jiang [view email]
[v1]
Thu, 12 Nov 2026 15:16:19 UTC (683 KB)
来源：arXiv cs.AI · arxiv.org