SlowGuess/NAACL_2022_OCR huggingface dataset:
To try to overcome this, we simultaneously utilize the prompts trained with three random seeds on the same dataset and take their intersection of activation states as the activated neurons into the similarity (ON) computation. This similarity is called  $\mathrm{ON_I}$ . By using it, the correlation score of ON can significantly raise as shown in Table 10.
D.4 Overlapping Rate of Activated Neurons in Different Layers
To further understand model stimulation in PLMs, we investigate ON in different layers of PLMs. Specifically, on  $\mathrm{RoBERTa_{BASE}}$ , we measure the similarity between different prompts with activation states of from 1 to 3 layers (Figure 8), from 4 to 6 layers (Figure 9), from 7 to 9 layers (Figure 10), from 10 to 12 layers (Figure 11), and all 12 layers (Figure 12), respectively.
We find that the activated neurons are common in the bottom layers but tend to be more task-specific in top layers, which is consistent with the findings of previous works (Liu et al., 2019a).
Figure 8: ON in 1 - 3 layers of RoBERTa<sub>BASE</sub>.
Figure 9: ON in 4 - 6 layers of RoBERTa<sub>BASE</sub>.
Figure 10: ON in 7 - 9 layers of RoBERTa<sub>BASE</sub>.
Figure 11: ON in 10 - 12 layers of RoBERTa<sub>BASE</sub>.
Figure 12: ON in all 12 layers of RoBERTa<sub>BASE</sub>.