Au sujet de ce document
- sl:arxiv_author :
- sl:arxiv_firstAuthor : Mengnan Du
- sl:arxiv_num : 2208.11857
- sl:arxiv_published : 2022-08-25T03:51:39Z
- sl:arxiv_summary : Large language models (LLMs) have achieved state-of-the-art performance on a
series of natural language understanding tasks. However, these LLMs might rely
on dataset bias and artifacts as shortcuts for prediction. This has
significantly hurt their Out-of-Distribution (OOD) generalization and
adversarial robustness. In this paper, we provide a review of recent
developments that address the robustness challenge of LLMs. We first introduce
the concepts and robustness challenge of LLMs. We then introduce methods to
identify shortcut learning behavior in LLMs, characterize the reasons for
shortcut learning, as well as introduce mitigation solutions. Finally, we
identify key challenges and introduce the connections of this line of research
to other directions.@en
- sl:arxiv_title : Shortcut Learning of Large Language Models in Natural Language Understanding: A Survey@en
- sl:arxiv_updated : 2022-08-25T03:51:39Z
- sl:bookmarkOf : https://arxiv.org/abs/2208.11857
- sl:creationDate : 2022-08-27
- sl:creationTime : 2022-08-27T10:39:46Z
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