Tougher Text, Smarter Models: Raising the Bar for Adversarial Defence Benchmarks (2025.coling-main)
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| Challenge: | Recent advances in natural language processing have highlighted the vulnerability of deep learning models to adversarial attacks. |
| Approach: | They propose a benchmark for textual adversarial defence that evaluates state-of-the-art defence mechanisms across diverse datasets, models, and tasks. |
| Outcome: | The proposed benchmark incorporates a wide range of datasets and evaluates state-of-the-art defence mechanisms. |
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Searching for an Effective Defender: Benchmarking Defense against Adversarial Word Substitution (2021.emnlp-main)
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Zongyi Li, Jianhan Xu, Jiehang Zeng, Linyang Li, Xiaoqing Zheng, Qi Zhang, Kai-Wei Chang, Cho-Jui Hsieh
| Challenge: | Existing methods to defend against adversarial word-substitution attacks have not been evaluated or compared in a systematic manner. |
| Approach: | They propose to compare different defense methods under representative adversarial attacks . they propose a method that improves the robustness of neural text classifiers against such attacks a . |
| Outcome: | The proposed method improves robustness of neural text classifiers against such attacks by a significant margin. |
Adversarial NLI: A New Benchmark for Natural Language Understanding (2020.acl-main)
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| Challenge: | a new large-scale NLI benchmark dataset is presented to test models on a variety of popular NLIs. |
| Approach: | They propose a large-scale NLI benchmark dataset that is iteratively compared with a human-and-model-in-the-loop procedure. |
| Outcome: | The proposed method can be applied in a never-ending learning scenario, becoming a moving target for NLU, rather than a static benchmark that will quickly saturate. |
Detection of Adversarial Examples in Text Classification: Benchmark and Baseline via Robust Density Estimation (2022.findings-acl)
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| Challenge: | Word-level adversarial attacks have shown success in NLP, decreasing performance of transformer-based models with smaller perturbation rate. |
| Approach: | They propose a dataset for four popular attack methods on four datasets and four models to encourage further research in this field. |
| Outcome: | The proposed baseline has the highest auc on 29 out of 30 dataset-attack-model combinations. |
Toward Stronger Textual Attack Detectors (2023.findings-emnlp)
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| Challenge: | despite the high performance of deep learning techniques, trained models remain vulnerable to adversarial attacks . authors present LAROUSSE, STAKEOUT and other approaches to detect adversarials . LARousSE is unsupervised, hyperparameter free and non-differentiable . |
| Approach: | They propose a framework to detect adversarial attacks and an extended benchmark to test them . they demonstrate that LAROUSSE outperforms previous methods and allows to identify interesting factor of detection rate variations. |
| Outcome: | The proposed framework outperforms existing methods and allows to identify interesting factor of detection rate variations. |
A Novel Metric for Measuring the Robustness of Large Language Models in Non-adversarial Scenarios (2024.findings-emnlp)
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| 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. |
From Hero to Zéroe: A Benchmark of Low-Level Adversarial Attacks (2020.aacl-main)
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| Challenge: | Adversarial attacks are label-preserving modifications to inputs of machine learning classifiers designed to fool machines but not humans. |
| Approach: | They propose to use a dataset to test the robustness of future NLP models to identify low-level adversarial attacks that are less realistic in typical applications such as social media. |
| Outcome: | The proposed dataset provides a benchmark for testing robustness of future more human-like NLP models. |
Robustness and Adversarial Examples in Natural Language Processing (2021.emnlp-tutorials)
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| 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 . |
A Closer Look into the Robustness of Neural Dependency Parsers Using Better Adversarial Examples (2021.findings-acl)
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| 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. |
Vulnerabilities of Large Language Models to Adversarial Attacks (2024.acl-tutorials)
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| Challenge: | This tutorial focuses on the vulnerabilities of Large Language Models to adversarial attacks . the tutorial lays the foundation by explaining safety-aligned models and concepts in cybersecurity . |
| Approach: | This tutorial lays the foundation by explaining safety-aligned LLMs and concepts in cybersecurity. |
| Outcome: | The tutorial lays the foundation by explaining safety-aligned models and concepts in cybersecurity. |
How do humans perceive adversarial text? A reality check on the validity and naturalness of word-based adversarial attacks (2023.acl-long)
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| Challenge: | Existing text adversarial attacks are impractical in real-world scenarios where humans are involved. |
| Approach: | They have surveyed 378 human participants about the perceptibility of text adversarial examples produced by state-of-the-art methods. |
| Outcome: | The proposed methods ignore the property of imperceptibility or study it under limited conditions. |