Paper recorded by Signals 4 on 2026-10-08 in cs.CL. Abstract reproduced from arXiv; link to the original below.
Published 2026-10-08 on arXiv · recorded by Signals 4 on 2026-10-09
Category: cs.CL · 自然语言处理 · first seen 2026-10-09
Supervised fine-tuning (SFT) adapts pretrained large language models (LLMs) to downstream tasks, but the required concepts can receive substantially different levels of pretrained support. Frequent concepts are more likely to be well learned, whereas rare concepts may remain weakly represented. We introduce a novel notion named prior barrier to quantify how strongly the pretrained model supports c