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Measure and Improve Robustness in NLP Models: A Survey

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arxiv 2112.08313 v2 pith:554R634V submitted 2021-12-15 cs.CL cs.LG

Xuezhi Wang , Haohan Wang , Diyi Yang This is my paper
classification cs.CLcs.LG
keywords robustnessmodelsimproveapplicationsbeendefinitionsincreasinglylines
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As NLP models achieved state-of-the-art performances over benchmarks and gained wide applications, it has been increasingly important to ensure the safe deployment of these models in the real world, e.g., making sure the models are robust against unseen or challenging scenarios. Despite robustness being an increasingly studied topic, it has been separately explored in applications like vision and NLP, with various definitions, evaluation and mitigation strategies in multiple lines of research. In this paper, we aim to provide a unifying survey of how to define, measure and improve robustness in NLP. We first connect multiple definitions of robustness, then unify various lines of work on identifying robustness failures and evaluating models' robustness. Correspondingly, we present mitigation strategies that are data-driven, model-driven, and inductive-prior-based, with a more systematic view of how to effectively improve robustness in NLP models. Finally, we conclude by outlining open challenges and future directions to motivate further research in this area.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Evaluation of Adversarial Robustness in Arabic Language Models

    cs.CL 2026-07 conditional novelty 5.0 of 10

    Arabic BERT-family sentiment models lose up to 92% accuracy under diacritics and 58% under conjunction attacks; paraphrase attacks cut accuracy by 76% on average, and adversarial training only partially helps.

  2. Coordinated Robustness Evaluation Framework for Vision-Language Models

    cs.CV 2025-06 conditional novelty 4.0 of 10

    A coordinated image-plus-text attack built on a surrogate multimodal encoder achieves 80-94% attack success against ViLT, BLIP, and GIT on VQA and visual reasoning, surpassing cited baselines.

  3. Evaluating and Improving Robustness in Large Language Models: A Survey and Future Directions

    cs.CL 2025-06 conditional novelty 3.0 of 10

    LLM robustness research is organized into adversarial robustness, out-of-distribution robustness, and evaluation, with an accompanying GitHub collection of papers.

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