It has been shown that finetuned transformers and other supervised detectors
effectively distinguish between human and machine-generated text in some
situations arXiv:2305.13242, but we find that even simple classifiers on top of
n-gram and part-of-speech features can achieve very robust performance on both
in- and out-of-domain data. To understand how this is possible, we analyze
machine-generated output text in five datasets, finding that LLMs possess
unique fingerprints that manifest as slight differences in the frequency of
certain lexical and morphosyntactic features. We show how to visualize such
fingerprints, describe how they can be used to detect machine-generated text
and find that they are even robust across textual domains. We find that
fingerprints are often persistent across models in the same model family (e.g.
llama-13b vs. llama-65b) and that models fine-tuned for chat are easier to
detect than standard language models, indicating that LLM fingerprints may be
directly induced by the training data.

通过分析 LLM 指纹，我们展示了即使是简单的分类器也能在训练领域内外的数据中实现非常强大的性能，从而有效区分人类生成的文本和机器生成的文本。

大型语言模型留下指纹

Your Large Language Models Are Leaving Fingerprints

Current supervised visual detectors, though impressive within their training
distribution, often fail to segment out-of-distribution scenes into their
constituent entities. Recent test-time adaptation methods use auxiliary
self-supervised losses to adapt the network parameters to each test example
independently and have shown promising results towards generalization outside
the training distribution for the task of image classification. In our work, we
find evidence that these losses can be insufficient for instance segmentation
tasks, without also considering architectural inductive biases. For image
segmentation, recent slot-centric generative models break such dependence on
supervision by attempting to segment scenes into entities in a self-supervised
manner by reconstructing pixels. Drawing upon these two lines of work, we
propose Slot-TTA, a semi-supervised instance segmentation model equipped with a
slot-centric inductive bias, that is adapted per scene at test time through
gradient descent on reconstruction or novel view synthesis objectives. We show
that test-time adaptation in Slot-TTA greatly improves instance segmentation in
out-of-distribution scenes. We evaluate Slot-TTA in several 3D and 2D scene
instance segmentation benchmarks and show substantial out-of-distribution
performance improvements against state-of-the-art supervised feed-forward
detectors and self-supervised test-time adaptation methods.

本研究提出一种基于自我监督的 Slot-TTA 模型，在测试时通过梯度下降对重构或新颖视角综合目标进行场景自适应调整，大幅提高了场景内实例分割的准确性，尤其在非训练数据中取得了显著的性能提升。