Investigating Selective Prediction Approaches Across Several Tasks in IID, OOD, and Adversarial Settings
This work addresses the need for reliable selective prediction in NLP systems, highlighting that current methods are incremental and task-dependent, which is crucial for researchers and practitioners aiming to improve model robustness.
The study systematically evaluated selective prediction approaches across 17 NLP datasets under IID, OOD, and adversarial settings, finding that none consistently outperformed the simple MaxProb baseline, with performance varying significantly across tasks.
In order to equip NLP systems with selective prediction capability, several task-specific approaches have been proposed. However, which approaches work best across tasks or even if they consistently outperform the simplest baseline 'MaxProb' remains to be explored. To this end, we systematically study 'selective prediction' in a large-scale setup of 17 datasets across several NLP tasks. Through comprehensive experiments under in-domain (IID), out-of-domain (OOD), and adversarial (ADV) settings, we show that despite leveraging additional resources (held-out data/computation), none of the existing approaches consistently and considerably outperforms MaxProb in all three settings. Furthermore, their performance does not translate well across tasks. For instance, Monte-Carlo Dropout outperforms all other approaches on Duplicate Detection datasets but does not fare well on NLI datasets, especially in the OOD setting. Thus, we recommend that future selective prediction approaches should be evaluated across tasks and settings for reliable estimation of their capabilities.