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pro vyhledávání: '"Meissner, Johannes Mario"'
Debiasing language models from unwanted behaviors in Natural Language Understanding tasks is a topic with rapidly increasing interest in the NLP community. Spurious statistical correlations in the data allow models to perform shortcuts and avoid unco
Externí odkaz:
http://arxiv.org/abs/2210.16079
The issue of shortcut learning is widely known in NLP and has been an important research focus in recent years. Unintended correlations in the data enable models to easily solve tasks that were meant to exhibit advanced language understanding and rea
Externí odkaz:
http://arxiv.org/abs/2209.01824
Natural Language Inference (NLI) datasets contain examples with highly ambiguous labels. While many research works do not pay much attention to this fact, several recent efforts have been made to acknowledge and embrace the existence of ambiguity, su
Externí odkaz:
http://arxiv.org/abs/2106.03020
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