学习捷径：论语言模型中自然语言理解的误导性承诺
Learning Shortcuts: On the Misleading Promise of NLU in Language Models
AI总结：
本文综述了大型语言模型中的捷径学习现象，指出其造成性能假象并缺乏泛化性，呼吁深化理解以提升NLU评估标准。
摘要：
大型语言模型（LLMs）的出现推动了自然语言处理领域性能的显著提升。然而，近期研究发现，LLMs在执行任务时常常诉诸于捷径，造成性能增强的假象，而其决策规则缺乏泛化能力。这一现象给准确评估LLMs中的自然语言理解带来了挑战。本文对该领域的相关研究进行了简明综述，并就捷径学习在语言模型评估（特别是针对NLU任务）中的影响提出了观点。本文呼吁投入更多研究努力，以加深我们对捷径学习的理解，促进更稳健语言模型的发展，并提高真实世界场景中NLU评估的标准。
英文摘要：
The advent of large language models (LLMs) has enabled significant performance gains in the field of natural language processing. However, recent studies have found that LLMs often resort to shortcuts when performing tasks, creating an illusion of enhanced performance while lacking generalizability in their decision rules. This phenomenon introduces challenges in accurately assessing natural language understanding in LLMs. Our paper provides a concise survey of relevant research in this area and puts forth a perspective on the implications of shortcut learning in the evaluation of language models, specifically for NLU tasks. This paper urges more research efforts to be put towards deepening our comprehension of shortcut learning, contributing to the development of more robust language models, and raising the standards of NLU evaluation in real-world scenarios.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
