The widespread use of Large Language Models (LLMs), celebrated for their
ability to generate human-like text, has raised concerns about misinformation
and ethical implications. Addressing these concerns necessitates the
development of robust methods to detect and attribute text generated by LLMs.
This paper investigates "Cross-Model Detection," evaluating whether a
classifier trained to distinguish between source LLM-generated and
human-written text can also detect text from a target LLM without further
training. The study comprehensively explores various LLM sizes and families,
and assesses the impact of conversational fine-tuning techniques on classifier
generalization. The research also delves into Model Attribution, encompassing
source model identification, model family classification, and model size
classification. Our results reveal several key findings: a clear inverse
relationship between classifier effectiveness and model size, with larger LLMs
being more challenging to detect, especially when the classifier is trained on
data from smaller models. Training on data from similarly sized LLMs can
improve detection performance from larger models but may lead to decreased
performance when dealing with smaller models. Additionally, model attribution
experiments show promising results in identifying source models and model
families, highlighting detectable signatures in LLM-generated text. Overall,
our study contributes valuable insights into the interplay of model size,
family, and training data in LLM detection and attribution.

通过探索跨模型检测、模型归因以及分类器的效果与模型规模之间的关系，该研究在大型语言模型的使用中提供了宝贵的见解。