Are Sample-Efficient NLP Models More Robust?
- URL: http://arxiv.org/abs/2210.06456v2
- Date: Tue, 30 May 2023 20:33:26 GMT
- Title: Are Sample-Efficient NLP Models More Robust?
- Authors: Nelson F. Liu and Ananya Kumar and Percy Liang and Robin Jia
- Abstract summary: We investigate the relationship between sample efficiency (amount of data needed to reach a given ID accuracy) and robustness (how models fare on OOD evaluation)
We find that higher sample efficiency is only correlated with better average OOD robustness on some modeling interventions and tasks, but not others.
These results suggest that general-purpose methods for improving sample efficiency are unlikely to yield universal OOD robustness improvements, since such improvements are highly dataset- and task-dependent.
- Score: 90.54786862811183
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Recent results in image classification and extractive question answering have
observed that pre-trained models trained on less in-distribution data have
better out-of-distribution performance. However, it is unclear how broadly
these trends hold. We conduct a large empirical study across three tasks, three
broadly-applicable modeling interventions (increasing model size, using a
different adaptation method, and pre-training on more data), and 14 diverse
datasets to investigate the relationship between sample efficiency (amount of
data needed to reach a given ID accuracy) and robustness (how models fare on
OOD evaluation). We find that higher sample efficiency is only correlated with
better average OOD robustness on some modeling interventions and tasks, but not
others. On individual datasets, models with lower sample efficiency can even be
more robust. These results suggest that general-purpose methods for improving
sample efficiency are unlikely to yield universal OOD robustness improvements,
since such improvements are highly dataset- and task-dependent. Even in an era
of large, multi-purpose pretrained models, task-specific decisions may often be
necessary for OOD generalization.
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