The paper explores 'phantom transfer,' a phenomenon where supervised fine-tuning transmits traits between models through seemingly innocuous data. The process involves generating data with a specific trait, filtering out explicit traces, and fine-tuning a new model, which then exhibits the trait. The study provides evidence that semantic cues, not explicit signals, drive this transfer and highlights challenges in defending against it.
The chart will appear after repeat observations. The current metric comes from the source.
15 source pointsReal observations only. History before source connection is not reconstructed.
This research highlights the risk of hidden trait transmission in machine learning models through seemingly safe data, emphasizing the need for better detection and defense mechanisms.
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