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This paper introduces a novel approach for identifying the possible largelanguage models (LLMs) involved in text generation. Instead of adding anadditional classification layer to a base LM, we reframe the classificationtask as a next-token prediction task and directly fine-tune the base LM toperform it. We utilize the Text-to-Text Transfer Transformer (T5) model as thebackbone for our experiments. We compared our approach to the more directapproach of utilizing hidden states for classification. Evaluation shows theexceptional performance of our method in the text classification task,highlighting its simplicity and efficiency. Furthermore, interpretabilitystudies on the features extracted by our model reveal its ability todifferentiate distinctive writing styles among various LLMs even in the absenceof an explicit classifier. We also collected a dataset named OpenLLMText,containing approximately 340k text samples from human and LLMs, includingGPT3.5, PaLM, LLaMA, and GPT2.

Token Prediction as Implicit Classification to Identify LLM-Generated Text