Supervised fine-tuning (SFT) is a standard way to adapt pretrained large language models to specific tasks. But not all concepts that a task requires are equally supported by the model's pretraining. A new arXiv paper, 'Overcoming Prior Barriers: Supervised Fine-Tuning under Long-Tail Distribution,' addresses this imbalance.
The authors point out that frequent concepts are more likely to have strong pretrained support, while rare or long-tail concepts may be under-supported. This creates a 'prior barrier' that can make SFT less effective for tasks that depend on uncommon knowledge. The paper's abstract suggests that overcoming this barrier is the central goal.
Because only the abstract is available in the source, the specific method is not described here. The significance lies in recognizing that SFT performance is not just about task data, but also about the alignment between task concepts and the model's pretraining distribution.