UKP-SQuARE v2: Explainability and Adversarial Attacks for Trustworthy QA (2022.aacl-demo)
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Rachneet Sachdeva, Haritz Puerto, Tim Baumgärtner, Sewin Tariverdian, Hao Zhang, Kexin Wang, Hossain Shaikh Saadi, Leonardo F. R. Ribeiro, Iryna Gurevych
| Challenge: | Question Answering (QA) systems rely on deep neural networks, which are difficult to interpret by humans. |
| Approach: | They propose an interpretable model that provides an explanation infrastructure for comparing models based on saliency maps and graph-based explanations. |
| Outcome: | The proposed methods can be used to compare models based on saliency maps and graph-based explanations. |
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UKP-SQUARE: An Online Platform for Question Answering Research (2022.acl-demo)
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Tim Baumgärtner, Kexin Wang, Rachneet Sachdeva, Gregor Geigle, Max Eichler, Clifton Poth, Hannah Sterz, Haritz Puerto, Leonardo F. R. Ribeiro, Jonas Pfeiffer, Nils Reimers, Gözde Şahin, Iryna Gurevych
| Challenge: | Recent advances in NLP and information retrieval have given rise to a diverse set of question answering tasks that are of different formats and require different model architectures and setups. |
| Approach: | They propose an extensible online QA platform that allows users to query and analyze a large collection of modern Skills via a user-friendly web interface and integrated behavioural tests. |
| Outcome: | The proposed tool allows users to query and analyze a large collection of modern Skills via a user-friendly web interface and integrated behavioural tests. |
UKP-SQuARE v3: A Platform for Multi-Agent QA Research (2023.acl-demo)
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Haritz Puerto, Tim Baumgärtner, Rachneet Sachdeva, Haishuo Fang, Hao Zhang, Sewin Tariverdian, Kexin Wang, Iryna Gurevych
| Challenge: | Current approaches to QA models are multi-dataset models, but combining expert agents can yield large performance gains over multi-agent models. |
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RobustQA: A Framework for Adversarial Text Generation Analysis on Question Answering Systems (2023.emnlp-demo)
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Yasaman Boreshban, Seyed Morteza Mirbostani, Seyedeh Fatemeh Ahmadi, Gita Shojaee, Fatemeh Kamani, Gholamreza Ghassem-Sani, Seyed Abolghasem Mirroshandel
| Challenge: | Question answering (QA) systems have reached human-level accuracy, but they are not robust enough and vulnerable to adversarial examples. |
| Approach: | They modified the attack algorithms widely used in text classification to fit them for QA systems. |
| Outcome: | The proposed framework is the first open-source toolkit for investigating textual adversarial attacks in QA systems. |
SHAP-Based Explanation Methods: A Review for NLP Interpretability (2022.coling-1)
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| Challenge: | Existing models with opacity problems have been proposed to address this problem. |
| Approach: | They propose a unified local-interpretability framework with a rigorous theoretical foundation on the game-theoretic concept of Shapley values. |
| Outcome: | The proposed framework is based on the Shapley-value-based model explanations. |
Beyond Model Interpretability: On the Faithfulness and Adversarial Robustness of Contrastive Textual Explanations (2022.findings-emnlp)
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| Challenge: | Existing explanations address the contrastive aspect of explanations but their extension to textual data is under-explored and there is little investigation on their vulnerabilities and limitations. |
| Approach: | They propose a novel evaluation scheme inspired by the faithfulness of explanations by extending the computation of three metrics to textual data and benchmarking POLYJUICE and MiCE on suggested metrics. |
| Outcome: | The proposed methods demonstrate that the connectedness of counterfactuals to their original counterparts is not obvious in both models. |
A Survey of the State of Explainable AI for Natural Language Processing (2020.aacl-main)
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| Challenge: | Recent years have seen significant advances in the quality of state-of-the-art models, but they have come at the expense of models becoming less interpretable. |
| Approach: | This survey examines the current state of Explainable AI within the domain of NLP . they detail the operations and explainability techniques currently available for generating explanations for NLP models . |
| Outcome: | This survey examines the state of explainable AI (XAI) within the domain of natural language processing . it focuses on the operations and explainability techniques currently available for NLP models . |
Can Rationalization Improve Robustness? (2022.naacl-main)
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| 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. |
DARE: Towards Robust Text Explanations in Biomedical and Healthcare Applications (2023.acl-long)
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| Challenge: | Several explainability methods have been shown to be brittle in the face of adversarial perturbations of their inputs in the image and generic textual domains. |
| Approach: | They propose to adapt existing attribution robustness estimation methods to take into account domain-specific plausibility and to train networks that display robust attributions. |
| Outcome: | The proposed methods are able to characterize domain-specific plausibility and provide robust explanations on biomedical datasets. |
PEDANTS: Cheap but Effective and Interpretable Answer Equivalence (2024.findings-emnlp)
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| Challenge: | Current short-form QA evaluations lack diverse styles of evaluation data and rely on expensive and slow LLMs. |
| Approach: | They propose a rubric for machine QA that is more stable than an exact match and neural methods. |
| Outcome: | The proposed evaluations improve on the existing short-form QA evaluations using the Trivia community. |
XAI-Attack: Utilizing Explainable AI to Find Incorrectly Learned Patterns for Black-Box Adversarial Example Creation (2024.lrec-main)
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| Challenge: | Adversarial examples can be used to trick machine learning models into making erroneous predictions, causing poorer insights and lower confidence in the information gathered. |
| Approach: | They propose a textual adversarial example method that identifies falsely learned word indicators by leveraging explainable AI methods as importance functions on incorrectly predicted instances. |
| Outcome: | The proposed method outperforms existing examples and training methods and shows baseline improvements of up to 23 percentage points on adversarial tasks. |