Machine learning (ML) systems in natural language processing (NLP) face significant challenges in generalizing to out-of-distribution (OOD) data, where the test distribution differs from the training data distribution. This poses important questions about the robustness of NLP models and their high accuracy, which may be artificially inflated due to their underlying sensitivity to systematic biases. Despite these challenges, there is a lack of comprehensive surveys on the generalization challenge from an OOD perspective in text classification. Therefore, this paper aims to fill this gap by presenting the first comprehensive review of recent progress, methods, and evaluations on this topic. We furth discuss the challenges involved and potential future research directions. By providing quick access to existing work, we hope this survey will encourage future research in this area.

翻译：机器学习（ML）系统在自然语言处理（NLP）中面临向分布外（OOD）数据泛化的重大挑战，此类场景下测试数据的分布与训练数据分布存在显著差异。这引发了关于NLP模型鲁棒性及其高准确率的深层问题——由于模型对系统性偏差存在潜在敏感性，其高准确率可能被人为夸大。尽管面临这些挑战，目前仍缺乏从分布外视角系统审视文本分类泛化难题的综合性综述。为此，本文首次对该领域的最新进展、现有方法与评估体系进行系统梳理，旨在填补这一研究空白。我们进一步探讨了当前面临的挑战与潜在未来研究方向。通过为现有研究成果提供快速查阅路径，本综述期望能推动该领域的后续研究发展。