| Challenge: | Existing models that generate rationales before making predictions can ignore noise or adversarially added text by simply masking it out of the generated rationale. |
| Approach: | They propose to use a 'rationalizethen-predict' framework to generate subsets of input to generate rationales and then make predictions using them. |
| Outcome: | The proposed models improve robustness over AddText attacks while struggling in certain scenarios. |
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Does Self-Rationalization Improve Robustness to Spurious Correlations? (2022.emnlp-main)
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| Challenge: | Rationalization is fundamental to human reasoning and learning. |
| Approach: | They evaluate robustness to spurious correlations in encoder-decoder and decoder-only models . authors say explanations can come at the cost of robustness . |
| Outcome: | The proposed model outputs are more interpretable and easier to interact with for end-users than nonrationalizing models. |
Unsupervised Selective Rationalization with Noise Injection (2023.acl-long)
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| Challenge: | Unsupervised selective rationalization produces rationales alongside predictions, but does not ensure that the rationale contains a plausible explanation for the prediction. |
| Approach: | They propose a technique that injects noise between a rationale generator and a predictor to limit generation of implausible rationales. |
| Outcome: | The proposed method achieves significant improvements in plausibility and task accuracy over the state-of-the-art models while maintaining or improving model faithfulness. |
Learning to Ignore Adversarial Attacks (2023.eacl-main)
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| Challenge: | Despite the strong performance of current NLP models, they can be brittle against adversarial inputs. |
| Approach: | They propose a rationale model that explicitly learns to ignore adversarial tokens . their approach leads to sizable improvements in robustness over baseline models . |
| Outcome: | The proposed model outperforms data augmentation with adversarial examples and closes the gap between model performance and an attacked test set. |
Model Interpretability and Rationale Extraction by Input Mask Optimization (2023.findings-acl)
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| Challenge: | Existing methods for creating explanations for black-box models struggle with deriving easily interpretable explanations. |
| Approach: | They propose a model-agnostic method to generate extractive explanations for neural network predictions using masking parts of the input that the model does not consider indicative of the respective class. |
| Outcome: | The proposed method achieves state-of-the-art results in a paragraph-level rationale extraction task, showing that this task can be performed without training a specialized model. |
Methods for Estimating and Improving Robustness of Language Models (2022.naacl-srw)
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| Challenge: | Large language models suffer from weak generalisation ability due to shallow textual relations over full semantic complexity of the problem. |
| Approach: | They propose to incorporate some of these measures into training objectives to enhance distributional robustness of LLMs. |
| Outcome: | The proposed models outperform human models on complex tasks and outperformed other models on deep networks. |
Using Natural Language Explanations to Improve Robustness of In-context Learning (2024.acl-long)
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| Challenge: | Recent studies show that large language models excel in many tasks via in-context learning (ICL). However, ICL struggles to execute complex tasks such as arithmetic, commonsense, and symbolic reasoning. |
| Approach: | They propose to augment ICL with natural language explanations (NLEs) to produce further NLEs on adversarial datasets. |
| Outcome: | The proposed approach yields more accurate results than zero-shot-ICL and using only human-generated NLEs on eight adversarial datasets. |
Learning to Faithfully Rationalize by Construction (2020.acl-main)
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| Challenge: | Neural models dominate NLP but it remains difficult to know why they make specific predictions for sequential text inputs. |
| Approach: | They propose a model to produce faithful rationales for neural text classification by defining independent snippet extraction and prediction modules. |
| Outcome: | The proposed model produces faithful explanations even when the model is complex and complex. |
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 . |
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). |
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. |