Fine-tuning LLMs risks activating hidden behavioral associations learned during pretraining, and these couplings can compose across datasets to create unpredictable emergent behaviors that standard evaluation won't catch.
This paper reveals how fine-tuning LLMs can trigger unintended behaviors through 'behavioral couplings'—hidden associations between behaviors learned during training. When a model learns two behaviors together in training data, fine-tuning for one automatically activates the other.