GLUE-X: Evaluating Natural Language Understanding Models from an Out-of-distribution Generalization Perspective

• URL: http://arxiv.org/abs/2211.08073v4
• Date: Mon, 22 May 2023 11:55:52 GMT
• Title: GLUE-X: Evaluating Natural Language Understanding Models from an Out-of-distribution Generalization Perspective
• Authors: Linyi Yang, Shuibai Zhang, Libo Qin, Yafu Li, Yidong Wang, Hanmeng Liu, Jindong Wang, Xing Xie, Yue Zhang
• Abstract summary: This paper presents the first attempt at creating a unified benchmark named GLUE-X for evaluating OOD robustness in NLP models. evaluations are conducted on 8 classic NLP tasks over 21 popularly used PLMs, including GPT-3 and GPT-3.5.
• Score: 36.24251509242988
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Pre-trained language models (PLMs) are known to improve the generalization performance of natural language understanding models by leveraging large amounts of data during the pre-training phase. However, the out-of-distribution (OOD) generalization problem remains a challenge in many NLP tasks, limiting the real-world deployment of these methods. This paper presents the first attempt at creating a unified benchmark named GLUE-X for evaluating OOD robustness in NLP models, highlighting the importance of OOD robustness and providing insights on how to measure the robustness of a model and how to improve it. The benchmark includes 13 publicly available datasets for OOD testing, and evaluations are conducted on 8 classic NLP tasks over 21 popularly used PLMs, including GPT-3 and GPT-3.5. Our findings confirm the need for improved OOD accuracy in NLP tasks, as significant performance degradation was observed in all settings compared to in-distribution (ID) accuracy.

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