SMART: Robust and Efficient Fine-Tuning for Pre-trained Natural Language Models through Principled Regularized Optimization
Table 6: Experiment Result for Each Round of ANLI.
['Method', 'Dev R1', 'Dev R2', 'Dev R3', 'Dev All', 'Test R1', 'Test R2', 'Test R3', 'Test All']
We evaluate the performance of SMART on each subset (i.e., R1,R2,R3) of ANLI dev and test set. + MNLI Williams et al. + SNLI Bowman et al. + FEVER Thorne et al. In the combined data setting, we obverse that SMARTRoBERTa-LARGE obtains the best performance compared with all the strong baselines, pushing benchmarks to 57.1%. In case of the RoBERTaLARGE baseline, SMARTRoBERTa-LARGE outperforms 3.4% absolute improvement on dev and 7.4% absolute improvement on test, indicating the robustness of SMART. We obverse that in the ANLI-only setting, SMARTRoBERTa-LARGE outperforms the strong RoBERTaLARGE baseline with a large margin, +5.2% (57.1% vs. 51.9%)