# Measure and Improve Robustness in NLP Models: A Survey

**Authors:** Xuezhi Wang, Haohan Wang, Diyi Yang  
**arXiv:** [2112.08313](https://arxiv.org/abs/2112.08313)  
**Date:** 2021-12-15  
**Summary page:** https://www.summarizepaper.com/en/paper/2112.08313v2/

## Key points

- Natural language processing (NLP) models have achieved state-of-the-art performances and gained wide applications in recent years.
- It is important to ensure the safe deployment of these models in the real world by ensuring their robustness against unseen or challenging scenarios.
- Robustness has been separately explored in applications like vision and NLP with various definitions, evaluation methods, and mitigation strategies in multiple lines of research.
- The paper titled "Measure and Improve Robustness in NLP Models: A Survey" aims to provide a unifying survey of how to define, measure, and improve robustness in NLP.
- The paper connects multiple definitions of robustness and unifies various lines of work on identifying robustness failures and evaluating models' robustness.
- Mitigation strategies presented are data-driven, model-driven, and inductive-prior based with a more systematic view of how to effectively improve robustness in NLP models.
- Open challenges that need further investigation include developing comprehensive benchmarks for evaluating model performance; transferability and validity of adversarial examples across different domains or tasks; creating a unified framework to evaluate and improve robustness across different NLP applications consistently; involving users or stakeholders in collecting a set of test cases where a system might perform well for the wrong reasons; understanding human perception processes better when it comes to NLP tasks; designing sanity tests; exploring connections between human-like linguistic generalization and NLP generalization.
- This survey provides valuable insights into the current state of research on robustness in NLP models.

## Summary

In recent years, natural language processing (NLP) models have achieved state-of-the-art performances and gained wide applications. However, it is increasingly important to ensure the safe deployment of these models in the real world by ensuring their robustness 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 methods and mitigation strategies in multiple lines of research. In this paper titled "Measure and Improve Robustness in NLP Models: A Survey," Xuezhi Wang from Google Research aims to provide a unifying survey of how to define, measure and improve robustness in NLP. The paper first connects multiple definitions of robustness and then unifies various lines of work on identifying robustness failures and evaluating models' robustness. Correspondingly, the paper presents 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. The paper also highlights some open challenges that need further investigation to motivate future research such as developing comprehensive benchmarks for evaluating model performance; transferability and validity of adversarial examples across different domains or tasks; creating a unified framework to evaluate and improve robustness across different NLP applications consistently; involving users or stakeholders in collecting a set of test cases where a system might perform well for the wrong reasons; understanding human perception processes better when it comes to NLP tasks; designing sanity tests; exploring connections between human-like linguistic generalization and NLP generalization. Overall, this survey provides valuable insights into the current state of research on robustness in NLP models and highlights some open challenges that need further exploration to improve model performance in real-world scenarios.