提出测试时自适应方法，应对生成文本分布漂移问题。
Hitting a Moving Target: Test-Time Adaptation for AI Text Detection under Continual Distribution Shift
- 利用推理时无标签样本的同质性进行半监督自适应
- 在对抗性与自然分布漂移下，检测准确率从24.1%提升至90.5%
- 适合部署后持续更新的AI文本检测场景
已部署的AI文本检测方法通常依赖训练时的标注数据（人工与生成文本）。但模型上线后会面临三类持续发生的分布偏移：对抗性人工化、新大模型发布、人类写作随时间演变。此时标注数据往往不可得。现有方法未利用大模型使用的关键信号——推理时的同质性。本文提出一种测试时自适应（TTA）方法，基于半监督学习，利用推理时观察到的无标签样本间的同质性来适应分布偏移。实验证明，先进监督检测器在面对分布偏移时系统失效；而我们的测试时自适应方法具有强鲁棒性。例如，商用模型Pangram仅能检测24.1%的对抗性生成文本，而本方法达到90.5%。结果表明，测试时自适应是真实环境中AI文本检测的可行方案。代码已公开于https://github.com/kkr36/llm_detection。
原文摘要 · Abstract (English)
Deployed approaches for AI text detection often rely on training-time access to labeled datasets of both human-written and AI-generated text. This approach is vulnerable to three types of distribution shifts that occur continually post-deployment, and for which labeled data is often unavailable: adversarial humanization, new LLMs being released, and temporal drift in human writing. Simultaneously, existing approaches do not leverage a key signal of LLM usage: inference-time homogeneity. We propose a test-time adaptation (TTA) approach, using semi-supervised learning, that adapts to distribution shifts by leveraging homogeneity among unlabeled samples observed at inference time. Empirically, we find that state-of-the-art supervised detectors systematically fail when they encounter distribution shifts in AI-generated and human writing, both adversarial and natural, while test-time adaptation with semi-supervised learning is largely robust; e.g., the commercial model Pangram detects just 24.1% of our adversarial AI-generated text, compared to 90.5% for our test-time approach. We establish that test-time adaptation is a promising framework for AI text detection in the wild. We publicly release our code (which includes code for model training, evaluation, and plots) at https://github.com/kkr36/llm_detection.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。