Do Explanations Help Users Detect Errors in Open-Domain QA? An Evaluation of Spoken vs. Visual Explanations (2021.findings-acl)
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| Challenge: | despite interest in explainable AI, there is increasing skepticism as to whether explanations are useful to end-users in downstream applications. |
| Approach: | They conduct user studies to measure whether explanations help users decide when to accept or reject an ODQA system's answer. |
| Outcome: | The proposed study shows that explanations outperform baselines across modalities but the best strategy varies with the modality. |
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Do explanations make VQA models more predictable to a human? (D18-1)
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| Challenge: | Existing explanations of a model's behavior are not used in interactive tasks like Visual Question Answering (VQA). |
| Approach: | They analyze existing explanations and their role in making a VQA model more predictable to a human by using human-in-the-loop approaches that treat the model as a black-box. |
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NoiseQA: Challenge Set Evaluation for User-Centric Question Answering (2021.eacl-main)
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| Challenge: | Question-Answering (QA) systems are deployed in the real world . a lack of research attention has been devoted to studying the issues that arise when people use QA systems. |
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On Evaluating Explanation Utility for Human-AI Decision Making in NLP (2024.findings-emnlp)
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| Challenge: | a lack of evidence that explanations help people in situations they are introduced for is a problem in NLP . prior work on explainability has focused on overcoming technical challenges and used proxy evaluations. |
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Modeling the Quality of Dialogical Explanations (2024.lrec-main)
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Milad Alshomary, Felix Lange, Meisam Booshehri, Meghdut Sengupta, Philipp Cimiano, Henning Wachsmuth
| Challenge: | Existing studies have focused on the interaction of explanation moves, dialogue acts, and topics in successful dialogues with expert explainers. |
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Evaluating Explainable AI: Which Algorithmic Explanations Help Users Predict Model Behavior? (2020.acl-main)
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| Challenge: | a new study examines the impact of algorithmic explanations on simulatability of machine learning models . a model is simulatable when a person can predict its behavior on new inputs . |
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Human-Centered Evaluation of Explanations (2022.naacl-tutorials)
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Jordan Boyd-Graber, Samuel Carton, Shi Feng, Q. Vera Liao, Tania Lombrozo, Alison Smith-Renner, Chenhao Tan
| Challenge: | This tutorial will provide an overview of human-centered evaluations of explanations . |
| Approach: | This tutorial will provide an overview of human-centered evaluations of explanations . it will introduce the psychological foundation of explanation and types of NLP explanations. |
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On the Interaction of Belief Bias and Explanations (2021.findings-acl)
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| Challenge: | Existing methods to evaluate explainability fail to account for belief biases affecting human performance . previous studies have shown that neural models can make confident predictions relying on artifacts . |
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An Empirical Study on Explanations in Out-of-Domain Settings (2022.acl-long)
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| Challenge: | Recent work in Natural Language Processing has focused on extracting faithful explanations . yet, little is known about how post-hoc explanations perform in out-of-domain settings . |
| Approach: | They propose to use a random baseline to evaluate out-of-domain post-hoc explanation faithfulness . they suggest select-then-predict models demonstrate comparable predictive performance in out- of-domain settings to full-text trained models. |
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QED: A Framework and Dataset for Explanations in Question Answering (2021.tacl-1)
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Matthew Lamm, Jennimaria Palomaki, Chris Alberti, Daniel Andor, Eunsol Choi, Livio Baldini Soares, Michael Collins
| Challenge: | Existing question answering systems provide no explanation of reasoning that leads to answer . linguistically informed, extensible framework provides explanations in question answering . |
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What if you said that differently?: How Explanation Formats Affect Human Feedback Efficacy and User Perception (2024.naacl-long)
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| Challenge: | Question answering models can often be black boxes, as their reasoning process is mostly opaque. |
| Approach: | They analyze the effect of rationales generated by QA models on user feedback and how well they enable users to understand and trust model answers. |
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