Double Perturbation: On the Robustness of Robustness and Counterfactual Bias Evaluation (2021.naacl-main)
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| 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. |
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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 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. |
Evaluating Neural Model Robustness for Machine Comprehension (2021.eacl-main)
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| 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. |
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On Evaluation of Adversarial Perturbations for Sequence-to-Sequence Models (N19-1)
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| Challenge: | Existing methods for assessing the robustness of sequence-to-sequence models have been ignored by the literature. |
| Approach: | They propose an evaluation framework for adversarial attacks on seq2seq models that takes the semantic equivalence of the pre- and post-perturbation input into account. |
| Outcome: | The proposed framework breaks the assumption that source perturbations should not result in changes in the expected output, but allows for meaning-preserving perturbations that change the output sequence. |
Evaluating the Robustness of Neural Language Models to Input Perturbations (2021.emnlp-main)
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| Challenge: | High-performance neural language models have achieved state-of-the-art results on a wide range of NLP tasks, but results for common benchmark datasets often do not reflect model reliability and robustness when applied to noisy, real-world data. |
| Approach: | They propose to implement character-level and word-level perturbation methods to simulate scenarios in which input texts may be slightly noisy or different from the data distribution on which NLP systems were trained. |
| Outcome: | The proposed methods simulate scenarios in which input texts may be slightly noisy or different from the data distribution on which NLP systems were trained. |
Towards Robustness of Text-to-SQL Models Against Natural and Realistic Adversarial Table Perturbation (2022.acl-long)
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| Challenge: | Existing Text-to-SQL parsers are vulnerable to perturbations in NL questions . we propose the Adversarial Table Perturbation (ATP) as a new attacking paradigm . |
| Approach: | They propose to use the Adversarial Table Perturbation to measure robustness of Text-to-SQL parsers against adversarial perturbations. |
| Outcome: | The proposed approach outperforms baseline methods in robustness evaluations on ADVETA and can be used in future projects. |
Interpreting the Robustness of Neural NLP Models to Textual Perturbations (2022.findings-acl)
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| Challenge: | Modern Natural Language Processing models are sensitive to input perturbations and their performance can decrease when applied to noisy data. |
| Approach: | They propose to explain the extent to which a model is affected by an unseen textual perturbation by the learnability of the perturbation. |
| Outcome: | The proposed model is better at identifying a perturbation (higher learnability) but worse at ignoring it (lower robustness). |
Perturbation Sensitivity Analysis to Detect Unintended Model Biases (D19-1)
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| Challenge: | Recent research shows that data-driven NLP models may inadvertently capture, reflect and sometimes amplify various social biases present in the language data they are trained on. |
| Approach: | They propose a generic evaluation framework that detects unintended model biases related to named entities and requires no new annotations or corpora. |
| Outcome: | The proposed framework detects unintended model biases related to named entities and requires no new annotations or corpora. |
Fighting Bias With Bias: Promoting Model Robustness by Amplifying Dataset Biases (2023.findings-acl)
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| Challenge: | Recent work sought to develop robust, unbiased models by filtering biased examples from training sets. |
| Approach: | They propose to filter out biased examples from training sets to improve models' performance. |
| Outcome: | The proposed evaluation framework is more challenging than the original dataset splits and even more challenging that hand-crafted challenge sets. |
Evaluating and Enhancing the Robustness of Code Pre-trained Models through Structure-Aware Adversarial Samples Generation (2023.findings-emnlp)
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| Challenge: | Pre-trained code models have made significant strides in the field of neural code intelligence, but they are susceptible to adversarial attacks that subtly modify the input sequence and can impair generalization. |
| Approach: | They propose a set of novel robustness evaluation methods based on the intrinsic structure of the code to explore the impact of imperceptible perturbation. |
| Outcome: | The proposed methods have demonstrated their effectiveness across a wide range of models and tasks, and are able to predict the performance of perturbed models. |