SlowGuess/NAACL_2022_OCR huggingface dataset:
the results for probes with different dropout rates and for varying counterfactual losses, but we had to choose the layer of the QA model at which to perform interventions.
Therefore, we created a validation suite based on the Intervention template, using new nouns, verbs, and adjectives. For dropout rates from 0.0 to 0.3, ranging over counterfactual losses, and layers from 1 to 7, we computed the QA model's F1 and Exact Match scores on the validation suite. These results are included in Table 8, and strongly suggested that performance, for all probes, was most increased via interventions at layer 4.
D Varying Dropout Rates
In the main paper, we reported included only some of the results for distance- and depth-based probe interventions. Here, we first show, in more detail, how increasing the dropout rate grows the causal effect with the QA attachment suite and distance
α/Loss	Layer	0.05	0.1	0.2	0.3
Dist. 0.0	1	71.9/59.4	72.7/60.9	73.4/60.9	73.4/60.9
2	69.5/56.3	71.9/60.9	71.9/60.9	71.9/59.4
3	71.1/60.9	71.1/59.4	71.9/59.4	71.9/59.4
4	71.9/62.5	72.6/60.4	71.9/59.4	73.4/60.9
5	68.8/57.8	68.8/56.3	72.7/60.9	73.4/62.5
6	68.8/57.8	69.5/59.4	71.9/60.9	72.7/62.5
7	70.3/60.9	70.3/60.9	72.6/62.5	72.6/62.5
Dist. 0.1	1	69.5/56.3	71.1/59.4	71.9/59.4	71.9/59.4
2	68.8/60.9	70.3/60.9	69.3/59.4	71.1/59.4
3	67.2/56.4	69.5/60.9	72.7/60.9	73.4/62.5
4	75.8/64.1	72.7/60.9	72.7/60.9	72.7/60.9
5	68.8/56.3	70.3/59.4	71.9/57.8	71.1/56.3
6	75.0/59.4	72.7/60.9	73.4/62.5	73.4/62.5
7	72.7/60.9	72.7/62.5	72.7/62.5	72.7/60.9
Dist. 0.2	1	69.5/54.7	70.3/56.3	72.7/59.4	73.4/60.9
2	73.4/60.9	74.2/59.4	74.2/62.5	74.2/62.5
3	70.3/59.4	69.5/56.3	71.1/57.8	71.9/57.8
4	74.2/65.6	75.0/65.6	75.8/65.6	75.0/64.1
5	71.1/62.5	71.9/64.1	71.1/62.5	71.9/60.9
6	73.4/62.5	71.8/59.4	74.2/62.5	74.2/62.5
7	71.9/59.4	73.4/62.5	72.7/62.5	72.7/60.9
Dist. 0.3	1	67.2/54.7	70.3/59.4	73.4/62.5	72.7/60.9
2	68.8/60.9	71.1/60.9	72.7/62.5	71.9/60.9
3	61.7/53.1	64.8/56.3	71.9/64.1	72.3/65.6
4	67.2/59.4	71.9/64.1	75.0/65.6	75.8/65.6
5	62.5/56.3	68.8/59.4	70.3/62.5	70.3/62.5
6	71.1/62.5	711/64.1	70.3/60.9	71.1/60.9
7	75.0/64.1	72.7/62.5	71.9/62.5	73.4/62.5
Table 8: Validation Coord. suite results (F1/Exact Match) using distance probes. For each probe type, we iterated over intervention layer and counterfactual loss value. The small validation suite was useful for rapid identification of good hyperparameter settings. All probes had the best performance at layer 4 (in bold).
probes of varying  $\alpha$ . Next, we include the mean causal effect plots for Mask and QA models using both types of probes on the 5 total suites.
First, we plotted an example of how increasing the dropout rate grew the causal effect in the QA attachment quite in Figure 8. We found that positive dropout values consistently outperformed probes with no dropout. Furthermore, for  $\alpha$  ranging from 0.1 to 0.4, increasing the dropout rate seemed to increase the effect size. Considering only interventions at layer 2, for example, vanilla probes shifted model predictions by at most  $2\%$  for different parses; for probes with dropout 0.5, probabilities shifted by roughly  $20\%$ .
Finally, we included results for all dropout rates and counterfactual losses in Figures 9 and 10.
E Probe Performance Metrics
In the main paper, we demonstrated the benefits of using dropout probes for creating counterfactual embeddings. One could hypothesize that the dropout enables better counterfactuals because the probes are prevented from overfitting to the training data. We found that that was not the case.
In Figure 11, we plotted probe performance metrics for the distance- and depth-based probes. For the distance probe, we reported the spearman correlation coefficient between predicted and actual pairwise distances between words in a sentence's parse tree. For the depth probe, we reported the accuracy of the probe in predicting the word at the root of the syntax tree. Both metrics were used in prior probing literature (Hewitt and Manning, 2019).
Figure 8: Dropout distance probes with dropout rates from 0.0 to 0.5 showed how, to a point, increasing the dropout rate increased the effect size for QA models on the Coord. suite.
We found that, while using non-linear probes boosted probe performance compared to linear
probes, adding dropout actually worsened probe performance. This suggests that the benefits from
QA Model Causal Effect on Attachment Suites Using Dropout Distance Probes
Figure 9: Mask mean causal effects using depth- (top) or distance-based (bottom) probes. Depth probes revealed smaller effects than distance-based probes, but a similar pattern of benefiting from lower counterfactual loss and higher dropout.
Figure 10: QA mean causal effects using depth-based (top) or distance-based (bottom) probes.
dropout in counterfactual generation arose from a phenomenon other than higher-performing probes.
F Scientific Artifacts
In this work, we built upon pre-existing scientific artifacts, including datasets and publicly-available code. Here, we briefly list their licenses and in
tended use cases. We used all artifacts for purely academic purposes.
The Penn TreeBank is licensed under the "LDC User Agreement for Non-Members" (Marcus et al., 1993). The dataset is commonly used in many academic settings (e.g., Hewitt and Manning (2019); Tucker et al. (2021)).
(a) Distance probe metrics
(b) Depth probe metrics
Figure 11: Metrics for the distance (left) and depth (right) probes showed that introducing dropout worsened probe performance as measured on the probe prediction tasks. Means over 5 trials plotted. All standard deviations less than 0.01.
The Stanford Question Answering Dataset 2.0 is under a creative commons license and is commonly used in academic settings (Rajpurkar et al., 2016).
The MNLI dataset is under an OANC license, "which allows all content to be freely used, modified, and shared under permissive terms" (Williams et al., 2018). The HANS dataset is under an MIT license (McCoy et al., 2019). Both datasets are commonly used in academic settings (McCoy et al., 2019).
The code we used to train the NLI and NLI-HANS models is under an Apache License 2.0 (Gao et al., 2021) and was developed for academic use.