  We present the findings of the Machine Learning Model Attribution Challenge.
Fine-tuned machine learning models may derive from other trained models without
obvious attribution characteristics. In this challenge, participants identify
the publicly-available base models that underlie a set of anonymous, fine-tuned
large language models (LLMs) using only textual output of the models.
Contestants aim to correctly attribute the most fine-tuned models, with ties
broken in the favor of contestants whose solutions use fewer calls to the
fine-tuned models' API. The most successful approaches were manual, as
participants observed similarities between model outputs and developed
attribution heuristics based on public documentation of the base models, though
several teams also submitted automated, statistical solutions.
