Measure and Improve Robustness in NLP Models: A Survey

• URL: http://arxiv.org/abs/2112.08313v1
• Date: Wed, 15 Dec 2021 18:02:04 GMT
• Title: Measure and Improve Robustness in NLP Models: A Survey
• Authors: Xuezhi Wang, Haohan Wang, Diyi Yang
• Abstract summary: robustness has been separately explored in applications like vision and NLP, with various definitions, evaluation and mitigation strategies in multiple lines of research. We first connect multiple definitions of robustness, then unify various lines of work on identifying robustness failures and evaluating models' robustness. 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.
• Score: 23.515869499536237
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Rigorous Probabilistic Guarantees for Robust Counterfactual Explanations [80.86128012438834]
  We show for the first time that computing the robustness of counterfactuals with respect to plausible model shifts is NP-complete. We propose a novel probabilistic approach which is able to provide tight estimates of robustness with strong guarantees.
  arXiv  Detail & Related papers  (2024-07-10T09:13:11Z)
• Exploring the Interplay of Interpretability and Robustness in Deep Neural Networks: A Saliency-guided Approach [3.962831477787584]
  Adversarial attacks pose a significant challenge to deploying deep learning models in safety-critical applications. Maintaining model robustness while ensuring interpretability is vital for fostering trust and comprehension in these models. This study investigates the impact of Saliency-guided Training on model robustness.
  arXiv  Detail & Related papers  (2024-05-10T07:21:03Z)
• The Risk of Federated Learning to Skew Fine-Tuning Features and Underperform Out-of-Distribution Robustness [50.52507648690234]
  Federated learning has the risk of skewing fine-tuning features and compromising the robustness of the model. We introduce three robustness indicators and conduct experiments across diverse robust datasets. Our approach markedly enhances the robustness across diverse scenarios, encompassing various parameter-efficient fine-tuning methods.
  arXiv  Detail & Related papers  (2024-01-25T09:18:51Z)
• Whispers of Doubt Amidst Echoes of Triumph in NLP Robustness [29.312873775442757]
  We conduct evaluations using (a) out-of-domain and challenge test sets, (b) behavioral testing with CheckLists, (c) contrast sets, and (d) adversarial inputs. We conclude that not only is the question of robustness in NLP as yet unresolved, but even some of the approaches to measure robustness need to be reassessed.
  arXiv  Detail & Related papers  (2023-11-16T09:09:32Z)
• Uncertainty Estimation of Transformers' Predictions via Topological Analysis of the Attention Matrices [3.1466086042810884]
  Transformer-based language models have set new benchmarks across a wide range of NLP tasks. reliably estimating the uncertainty of their predictions remains a significant challenge. We tackle these limitations by harnessing the geometry of attention maps across multiple heads and layers to assess model confidence. Our method significantly outperforms existing uncertainty estimation techniques on benchmarks for acceptability judgments and artificial text detection.
  arXiv  Detail & Related papers  (2023-08-22T09:17:45Z)
• On the Robustness of Aspect-based Sentiment Analysis: Rethinking Model, Data, and Training [109.9218185711916]
  Aspect-based sentiment analysis (ABSA) aims at automatically inferring the specific sentiment polarities toward certain aspects of products or services behind social media texts or reviews. We propose to enhance the ABSA robustness by systematically rethinking the bottlenecks from all possible angles, including model, data, and training.
  arXiv  Detail & Related papers  (2023-04-19T11:07:43Z)
• GLUECons: A Generic Benchmark for Learning Under Constraints [102.78051169725455]
  In this work, we create a benchmark that is a collection of nine tasks in the domains of natural language processing and computer vision. We model external knowledge as constraints, specify the sources of the constraints for each task, and implement various models that use these constraints.
  arXiv  Detail & Related papers  (2023-02-16T16:45:36Z)
• A Comprehensive Evaluation Framework for Deep Model Robustness [44.20580847861682]
  Deep neural networks (DNNs) have achieved remarkable performance across a wide area of applications. They are vulnerable to adversarial examples, which motivates the adversarial defense. This paper presents a model evaluation framework containing a comprehensive, rigorous, and coherent set of evaluation metrics.
  arXiv  Detail & Related papers  (2021-01-24T01:04:25Z)
• A general framework for defining and optimizing robustness [74.67016173858497]
  We propose a rigorous and flexible framework for defining different types of robustness properties for classifiers. Our concept is based on postulates that robustness of a classifier should be considered as a property that is independent of accuracy. We develop a very general robustness framework that is applicable to any type of classification model.
  arXiv  Detail & Related papers  (2020-06-19T13:24:20Z)
• SAMBA: Safe Model-Based & Active Reinforcement Learning [59.01424351231993]
  SAMBA is a framework for safe reinforcement learning that combines aspects from probabilistic modelling, information theory, and statistics. We evaluate our algorithm on a variety of safe dynamical system benchmarks involving both low and high-dimensional state representations. We provide intuition as to the effectiveness of the framework by a detailed analysis of our active metrics and safety constraints.
  arXiv  Detail & Related papers  (2020-06-12T10:40:46Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.