Avoiding Reasoning Shortcuts: Adversarial Evaluation, Training, and Model Development for Multi-Hop QA
Table 1: EM scores after training on the regular data or on the adversarial training set Add4Docs-Rand, and evaluation on the regular dev set or the Add4Docs-Rand adv-dev set. “1-hop Base” and ”2-hop” do not have sentence-level supporting-facts supervision.
['Train Eval', 'Reg', 'Reg Adv', 'Adv Reg', 'Adv']
In our main experiment, we compare four models’ performance on the regular HotpotQA and Add4Docs-Rand dev sets, when trained on two different training sets (regular or adversarial), respectively. As shown in the first row, the single-hop baseline trained on regular data performs poorly on the adversarial evaluation, suggesting that it is indeed exploiting the reasoning shortcuts instead of actually performing the multi-hop reasoning in locating the answer. After we add the supporting fact supervision However, this score is still more than 9 points lower than the model’s performance on the regular evaluation. Next, the 2-hop bi-attention model with the control unit obtains a higher EM score than the baseline in the adversarial evaluation, demonstrating better robustness against the adversaries. After this 2-hop model is additionally supervised to predict the sentence-level supporting facts, the performance in both regular and adversarial evaluation decreases a bit, but still outperforms both baselines in the regular evaluation (with stat. significance). One possible explanation for this performance drop is that the 2-hop model without the extra task of predicting supporting facts overfits to the task of the final answer prediction, thus achieving higher scores.