Out-of-Distribution Generalization in Natural Language Processing: Past, Present, and Future

Linyi Yang, Yaoxian Song, Xuan Ren, Chenyang Lyu, Yidong Wang, Jingming Zhuo, Lingqiao Liu, Jindong Wang, Jennifer Foster, Yue Zhang

Submission Type: Regular Long Paper

Submission Track: Machine Learning for NLP

Submission Track 2: Theme Track: Large Language Models and the Future of NLP

Keywords: Out-of-Distribution Generalization; OOD Robustness

TL;DR: We present the first comprehensive review of recent progress, methods, and evaluations on the topic of OOD generalization in natural language understanding.

Abstract: 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 natural language understanding. Therefore, this paper aims to fill this gap by presenting the first comprehensive review of recent progress, methods, and evaluations on this topic. We further discuss the challenges involved and potential future research directions. By providing convenient access to existing work, we hope this survey will encourage future research in this area.

Submission Number: 1585