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A Breakdown of MRQA Results
Table 6 shows the breakdown of results on the 12 MRQA in- and out- of domain evaluation sets.
B Combining with SQuAD1.1
Bartolo et al. (2020) and subsequent works find that the performance degradation in the original evaluation setting when training on adversarially-collected data only is mitigated by also including some of the original training data. To investigate this further, we combine and shuffle the training datasets collected in each of the experimental settings with 2k SQuAD1.1 examples for a total of 4k training examples per experiment.
The baseline results in Table 7 show that this results in similar performance, if slightly improved on the SQuAD dev set, when using some adversarially-collected data. We also show the results for the other experimental settings in Tables 8, 9 and 10, noting very similar performance variation between settings as those reported earlier.
C Adversarial Robustness of ELECTRA and RoBERTa
Table 11 shows adversarial robustness performance evaluated on the AddSent and AddOneSent evaluation datasets introduced by Jia and Liang (2017). We observe that even when trained only on SQuAD1.1, ELECTRA performs considerably better than RoBERTa in this setting, suggesting that it is substantially more robust "out of the box".
All experiments were run on single NVIDIA Tesla P100 GPUs. Models were trained for up to 14 epochs each taking approximately 2 hours to complete training. Best model checkpoints and hyperparameters were tuned for each experimental setting. The final model selected for each setting was based on validation performance across the SQuAD and AdversarialQA development sets. The time taken for evaluation of the final models on each of the AdversarialQA test sets and the MRQA datasets was dependent on the number of examples.
GAA Details	Adv?	MRQA in-domain	MRQA out-of-domain
HotpotQA	NQs	NewsQA	SearchQA	SQuAD	TriviaQA	BioASQ	DROP	DuoRC	RACE	RelExt.	TextbookQA
-	X	64.5	58.9	51.7	16.5	84.9	55.9	61.5	28.1	50.4	38.1	81.9	31.3
-	✓	65.3	65.4	57.2	36.6	85.0	62.2	66.9	43.4	55.5	44.1	83.4	41.1
SQuAD (Likelihood)	X	58.0	61.5	53.9	23.3	85.4	59.5	64.0	32.5	54.2	36.3	81.6	33.4
SQuAD (Adversarial)	X	56.7	63.8	55.4	37.4	84.1	55.4	60.7	30.4	53.9	38.8	75.5	39.3
SQuAD (Uncertainty)	X	62.3	62.0	53.5	26.3	83.4	57.6	63.0	30.9	50.5	37.0	79.6	32.2
SQuAD (Likelihood)	✓	67.3	62.9	56.9	30.3	86.7	60.8	66.1	39.0	54.7	43.2	81.8	41.8
SQuAD (Adversarial)	✓	60.5	61.6	51.4	30.8	83.3	59.7	62.5	38.1	52.7	39.9	80.7	39.5
SQuAD (Uncertainty)	✓	62.7	65.3	55.9	32.9	86.3	63.8	65.2	40.4	56.4	44.4	81.3	46.0
AdvQA (Likelihood)	✓	63.5	62.6	53.3	23.5	84.9	59.9	66.7	49.5	53.3	41.5	83.7	39.4
AdvQA (Adversarial)	✓	60.8	63.2	52.9	33.2	83.8	59.5	63.9	44.8	53.4	40.8	81.4	43.2
AdvQA (Uncertainty)	✓	62.3	61.8	53.3	26.0	83.6	64.0	62.5	47.6	52.9	39.4	81.8	32.9
Combined (Likelihood)	✓	61.6	62.6	56.1	21.3	85.0	58.8	67.7	46.9	56.5	43.3	79.1	39.6
Combined (Adversarial)	✓	60.8	60.4	51.8	30.0	82.7	55.7	61.2	42.6	53.5	38.5	79.6	37.4
Combined (Uncertainty)	✓	64.7	64.2	53.5	27.3	85.4	59.1	64.4	45.5	49.2	41.1	83.5	40.6
Results below are for the settings with answer prompting
AdvQA (Likelihood)	✓	60.4	63.9	51.8	26.8	83.5	56.9	65.8	48.4	51.7	42.5	81.0	38.0
AdvQA (Adversarial)	✓	60.0	63.8	51.3	25.0	83.7	60.4	65.0	48.6	49.9	40.4	83.3	31.0
AdvQA (Uncertainty)	✓	62.7	64.0	51.2	32.9	84.9	58.3	66.3	47.0	45.4	42.3	83.0	37.9
Combined (Likelihood)	✓	63.4	63.9	55.1	24.5	83.2	60.6	66.7	47.0	55.9	39.3	82.2	34.9
Combined (Adversarial)	✓	62.0	63.7	51.6	18.2	83.5	60.6	64.6	48.5	53.4	40.4	83.8	35.3
Combined (Uncertainty)	✓	60.6	62.9	54.2	25.0	83.6	59.2	63.3	44.4	52.5	41.6	80.3	35.3
Table 6: Result breakdown for all twenty experiment modes on the MRQA evaluation sets.
Adversary-in-the-loop?	SQuADdev	DBiDAF	DBERT	DRoBERTa	MRQA
X	88.9	49.8	28.6	22.7	56.1
✓	89.3	53.2	34.1	27.3	60.1
Table 7: Baseline results comparing standard and adversarial data collection. Downstream evaluation is measured by training an ELECTRALarge QA model on each of the collected datasets combined with 2k SQuAD training examples (for a total of 4k examples) and evaluating  $\mathrm{F}_1$  scores on the SQuAD1.1 dev set, the AdversarialQA test sets, and the MRQA dev sets for domain generalisation.
Sampling Strategy	SQuADdev	DBiDAF	DBERT	DRoBERTa	MRQA
Likelihood	88.9	49.2	29.0	22.8	56.7
Adversarial	88.7	52.0	30.1	24.5	58.1
Uncertainty	88.5	50.0	29.7	22.8	57.1
Table 8: Results for the investigation into supporting standard data collection using GAAs when combining with 2k SQuAD training examples. There is no adversarial QA model in the loop in this setting.
GAA Training	Sampling	SQuADdev	DBiDAF	DBERT	DRoBERTa	MRQA
SQuAD	Likelihood	89.4	51.4	31.7	24.1	58.8
SQuAD	Adversarial	88.4	50.9	31.8	23.2	59.3
SQuAD	Uncertainty	89.6	53.8	31.6	25.3	59.0
AdversarialQA	Likelihood	89.3	52.4	38.1	30.2	60.6
AdversarialQA	Adversarial	88.8	54.0	34.5	27.0	59.3
AdversarialQA	Uncertainty	88.8	54.5	39.2	30.0	58.3
Combined	Likelihood	88.9	54.6	37.4	27.7	58.7
Combined	Adversarial	89.0	53.2	34.9	25.7	58.3
Combined	Uncertainty	88.9	54.4	35.9	26.9	57.8
Table 9: Results for the investigation into supporting adversarial data collection using GAAs when combining with 2k SQuAD training examples. We investigate three different GAA training dataset sources, and three sampling strategies. The adversarial QA model used in the annotation loop is identical for all settings.
GAA Training	Sampling	SQUADdev	DBiDAF	DBERT	DRoBERTa	MRQA
AdversarialQA	Likelihood	89.2	53.9	43.4	31.9	59.7
AdversarialQA	Adversarial	89.1	53.4	36.4	28.0	58.8
AdversarialQA	Uncertainty	88.5	55.4	37.6	27.5	59.0
Combined	Likelihood	89.2	55.0	38.4	29.8	61.0
Combined	Adversarial	88.6	54.7	37.7	29.4	59.4
Combined	Uncertainty	88.8	53.1	32.6	26.7	57.5
Table 10: Results for the investigation into supporting adversarial data collection using GAAs equipped with answer prompting when combining with 2k SQuAD training examples. We investigate two different GAA training dataset sources, and three sampling strategies. The adversarial QA model-in-the-loop is identical for all settings.
Model	Training Data	SQuADdev	AddSent	AddOneSent
BERTLarge	SQuAD	90.3	73.7	80.3
SQuAD + AdversarialQA	93.3	80.1	85.2
RoBERTaLarge	SQuAD	93.5	82.4	86.9
SQuAD + AdversarialQA	92.5	83.4	86.7
SQuAD + AdversarialQA + SynQA	94.8	86.0	89.0
SQuAD + AdversarialQA + SynQAExt	94.9	87.1	90.1
ELECTRALarge	SQuAD	94.4	85.0	89.0
SQuAD + AdversarialQA	94.7	86.1	89.9
SQuAD + AdversarialQA + SynQA	94.8	85.7	89.2
Table 11: Word-overlap  $\mathrm{F}_{1}$  results for BERT, RoBERTa, and ELECTRA on the SQuAD1.1 dev set and the AddSent and AddOneSent adversarial evaluation sets (Jia and Liang, 2017).