Evaluating Explanations: How Much Do Explanations from the Teacher Aid Students? (2022.tacl-1)
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Danish Pruthi, Rachit Bansal, Bhuwan Dhingra, Livio Baldini Soares, Michael Collins, Zachary C. Lipton, Graham Neubig, William W. Cohen
| Challenge: | Existing methods to explain predictions by highlighting salient features are often unstated. |
| Approach: | They propose a framework to quantify the value of explanations via the accuracy gains that they confer on a student model trained to simulate a teacher model. |
| Outcome: | The proposed framework allows principled, automatic, model-agnostic evaluation of attributions. |
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Brihi Joshi, Keyu He, Sahana Ramnath, Sadra Sabouri, Kaitlyn Zhou, Souti Chattopadhyay, Swabha Swayamdipta, Xiang Ren
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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 . |
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| Challenge: | Attention mechanisms are ubiquitous components in neural network architectures and are often claimed to confer interpretability. |
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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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F1 is Not Enough! Models and Evaluation Towards User-Centered Explainable Question Answering (2020.emnlp-main)
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| Challenge: | Existing models and evaluation settings have shortcomings regarding the coupling of answer and explanation which might cause serious issues in user experience. |
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| Challenge: | despite rapid progress in multihop question-answering, models still have trouble explaining why an answer is correct. |
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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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| Challenge: | a large corpus of domain-expert relevance ratings augments a corpus for compositional explanations . a writer's study shows that the evaluations of compositional inference models underestimate performance . |
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Are Human Explanations Always Helpful? Towards Objective Evaluation of Human Natural Language Explanations (2023.acl-long)
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| Challenge: | Human-annotated labels and explanations are critical for training explainable NLP models. |
| Approach: | They propose a metric that measures the usefulness of an explanation for model performance at both fine-tuning and inference. |
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