This paper introduces a novel approach for identifying the possible large
language models (LLMs) involved in text generation. Instead of adding an
additional classification layer to a base LM, we reframe the classification
task as a next-token prediction task and directly fine-tune the base LM to
perform it. We utilize the Text-to-Text Transfer Transformer (T5) model as the
backbone for our experiments. We compared our approach to the more direct
approach of utilizing hidden states for classification. Evaluation shows the
exceptional performance of our method in the text classification task,
highlighting its simplicity and efficiency. Furthermore, interpretability
studies on the features extracted by our model reveal its ability to
differentiate distinctive writing styles among various LLMs even in the absence
of an explicit classifier. We also collected a dataset named OpenLLMText,
containing approximately 340k text samples from human and LLMs, including
GPT3.5, PaLM, LLaMA, and GPT2.

本文介绍了一种新颖的方法，用于识别可能参与文本生成的大规模语言模型（LLMs）。我们采用了一种将分类任务重新构建为下一个标记预测任务的方法，并直接微调基本语言模型来执行此任务。我们使用 Text-to-Text Transfer Transformer（T5）模型作为实验的基础，并将我们的方法与利用隐藏状态进行分类的更直接方法进行了比较。评估结果显示了我们的方法在文本分类任务中的卓越性能，突出了其简单性和效率。此外，对我们模型提取的特征进行的可解释性研究揭示了其能够在没有显式分类器的情况下区分不同 LLMs 之间的独特写作风格。我们还收集了一个名为 OpenLLMText 的数据集，其中包含来自人类和 LLMs（包括 GPT3.5、PaLM、LLaMA 和 GPT2）的约 340k 个文本样本。