| Challenge: | Experimental results show that self-attentive neural models are more robust against adversarial perturbations compared to recurrent neural networks. |
| Approach: | They propose an adversarial attack algorithm that generates more natural adversarials . they propose to use the attention mechanism to learn a context-dependent representation . |
| Outcome: | The proposed attack algorithm generates more natural adversarial examples that could mislead models but not humans. |
Similar Papers
A Closer Look into the Robustness of Neural Dependency Parsers Using Better Adversarial Examples (2021.findings-acl)
Copied to clipboard
| Challenge: | Neural network-based models have been successful in a wide range of NLP tasks, but their performance is undermined by adversarial examples that would pose no confusion for humans. |
| Approach: | They propose a method to generate high-quality adversarial examples with a higher number of candidate generators and stricter filters and then verify their quality using automatic and human evaluations. |
| Outcome: | The proposed method improves the robustness of English parsing models by relying on adversarial training and model ensembling. |
Evaluating and Enhancing the Robustness of Neural Network-based Dependency Parsing Models with Adversarial Examples (2020.acl-main)
Copied to clipboard
| Challenge: | Previously studies focused on semantic tasks such as sentiment analysis, question answering and reading comprehension. |
| Approach: | They propose two approaches to study where and how adversarial examples exist in dependency parsing . they use a state-of-the-art parser to find adversarials in existing texts . |
| Outcome: | The proposed approaches show that adversarial examples exist in dependency parsing . they show that up to 77% of input examples admit adversarials . |
Robustness and Adversarial Examples in Natural Language Processing (2021.emnlp-tutorials)
Copied to clipboard
| Challenge: | This tutorial aims to raise awareness of practical concerns about NLP robustness . it aims at addressing the weaknesses of NLP systems when faced with adversarial inputs and data with a distribution shift . |
| Approach: | This tutorial aims to bring awareness of practical concerns about NLP robustness . it reviews recent studies on analyzing the weakness of NLP systems when facing adversarial inputs . |
| Outcome: | This tutorial aims to bring awareness of practical concerns about NLP robustness . it will examine the weaknesses of NLP systems when faced with adversarial inputs and data with a distribution shift . |
Evaluating Neural Model Robustness for Machine Comprehension (2021.eacl-main)
Copied to clipboard
| Challenge: | evaluating model robustness to adversarial attacks can provide deeper understanding of how deep neural networks work and what kind of linguistic information is actually captured by neural networks. |
| Approach: | They propose a method for strategic sentence-level perturbations to evaluate model robustness to adversarial attacks using character and word perturbations. |
| Outcome: | The proposed model improves model performance during adversarial attacks by using ensembles and predicts errors in adversarials. |
Robust Machine Comprehension Models via Adversarial Training (N18-2)
Copied to clipboard
| Challenge: | Existing models for the Stanford Question Answering Dataset suffer from a 50% decrease in F1 score during adversarial evaluation based on AddSent. |
| Approach: | They propose an alternative adversary-generation algorithm, AddSentDiverse, that significantly increases the variance within the adversarial training data by providing effective examples that punish the model for making certain superficial assumptions. |
| Outcome: | The proposed algorithm can achieve a 36.5% increase in F1 score while maintaining performance on the regular SQuAD task. |
A Novel Metric for Measuring the Robustness of Large Language Models in Non-adversarial Scenarios (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Using large language models, we evaluated their robustness on multiple datasets. |
| Approach: | They propose a new metric for assessing model robustness by empirical evaluation of several models on multiple datasets. |
| Outcome: | The proposed metric is based on a set of datasets that are constructed by introducing naturally-occurring, non-malicious perturbations or by generating semantically equivalent paraphrases of input questions or statements. |
Self-regulation: Employing a Generative Adversarial Network to Improve Event Detection (P18-1)
Copied to clipboard
| Challenge: | Recent studies show that neural networks can be used for event detection but can be contaminated by spurious features. |
| Approach: | They propose a self-regulated learning approach by utilizing a generative adversarial network to generate spurious features. |
| Outcome: | The proposed method is highly effective and adaptable on the ACE 2005 and TAC-KBP 2015 corpora. |
Whispers of Doubt Amidst Echoes of Triumph in NLP Robustness (2024.naacl-long)
Copied to clipboard
| Challenge: | Existing approaches to measure robustness are problematic, and out-of-domain evaluations are no longer relevant. |
| Approach: | They examine models of different sizes spanning different architectural choices and pretraining objectives. |
| Outcome: | The results show that not all out-of-domain tests provide insight into robustness . merely scaling models does not make them adequately robust . |
Bias in the Mirror : Are LLMs opinions robust to their own adversarial attacks (2025.acl-long)
Copied to clipboard
| Challenge: | Existing work on large language models lacks robustness, highlighting the limitations of such models. |
| Approach: | They propose a novel approach where two LLMs engage in self-debate to persuade a neutral version of the model. |
| Outcome: | The proposed approach examines whether large language models are robust during interactions and whether they are susceptible to reinforcing misinformation or shifting to harmful viewpoints. |
Double Perturbation: On the Robustness of Robustness and Counterfactual Bias Evaluation (2021.naacl-main)
Copied to clipboard
| Challenge: | Recent studies show that NLP models are vulnerable to adversarial perturbations such as synonym substitutions or syntax-guided paraphrasing. |
| Approach: | They propose a “double perturbation” framework to uncover model weaknesses beyond the test dataset. |
| Outcome: | The proposed attack achieves high success rates on both original and robustly trained CNNs and Transformers. |