  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," by 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, quantization,
and watermarking on classifier generalization. The research also explores Model
Attribution, encompassing source model identification, model family, and model
size classification, in addition to quantization and watermarking detection.
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, with particularly
remarkable outcomes in watermarking detection, while no detectable signatures
of quantization were observed. Overall, our study contributes valuable insights
into the interplay of model size, family, and training data in LLM detection
and attribution.
