Shortcut Learning of Large Language Models in Natural Language Understanding

• URL: http://arxiv.org/abs/2208.11857v2
• Date: Sun, 7 May 2023 23:55:09 GMT
• Title: Shortcut Learning of Large Language Models in Natural Language Understanding
• Authors: Mengnan Du, Fengxiang He, Na Zou, Dacheng Tao and Xia Hu
• Abstract summary: Large language models (LLMs) have achieved state-of-the-art performance on a series of natural language understanding tasks. They might rely on dataset bias and artifacts as shortcuts for prediction. This has significantly affected their generalizability and adversarial robustness.
• Score: 119.45683008451698
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 affected their generalizability and adversarial robustness. In this paper, we provide a review of recent developments that address the shortcut learning and robustness challenge of LLMs. We first introduce the concepts of shortcut learning of language models. We then introduce methods to identify shortcut learning behavior in language models, characterize the reasons for shortcut learning, as well as introduce mitigation solutions. Finally, we discuss key research challenges and potential research directions in order to advance the field of LLMs.

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