  Finetuning language models (LMs) is crucial for adapting the models to
downstream data and tasks. However, full finetuning is usually costly. Existing
work, such as parameter-efficient finetuning (PEFT), often focuses on
\textit{how to finetune} but neglects the issue of \textit{where to finetune}.
As a pioneering work on answering where to finetune (at the layer level), we
conduct a semantic analysis of the LM inference process. We first propose a
virtual transition of the latent representation and then trace its factual
transition. Based on the deviation in transitions, we estimate the gain of
finetuning each model layer, and further, narrow down the scope for finetuning.
We perform extensive experiments across well-known LMs and datasets. The
results show that our approach is effective and efficient, and outperforms the
existing baselines. Our approach is orthogonal to existing efficient
techniques, such as PEFT methods, offering practical values on LM finetuning.
