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"Why Should I Trust You?": Explaining the Predictions of Any Classifier
TLDR
LIME is proposed, a novel explanation technique that explains the predictions of any classifier in an interpretable and faithful manner, by learning aninterpretable model locally varound the prediction.
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Anchors: High-Precision Model-Agnostic Explanations
We introduce a novel model-agnostic system that explains the behavior of complex models with high-precision rules called anchors, representing local, "sufficient" conditions for predictions. We…
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DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs
TLDR
A new reading comprehension benchmark, DROP, which requires Discrete Reasoning Over the content of Paragraphs, and presents a new model that combines reading comprehension methods with simple numerical reasoning to achieve 51% F1.
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Beyond Accuracy: Behavioral Testing of NLP Models with CheckList
Although measuring held-out accuracy has been the primary approach to evaluate generalization, it often overestimates the performance of NLP models, while alternative approaches for evaluating models…
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Knowledge Enhanced Contextual Word Representations
TLDR
After integrating WordNet and a subset of Wikipedia into BERT, the knowledge enhanced BERT (KnowBert) demonstrates improved perplexity, ability to recall facts as measured in a probing task and downstream performance on relationship extraction, entity typing, and word sense disambiguation.
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Generating Natural Adversarial Examples
TLDR
This paper proposes a framework to generate natural and legible adversarial examples that lie on the data manifold, by searching in semantic space of dense and continuous data representation, utilizing the recent advances in generative adversarial networks.
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Universal Adversarial Triggers for Attacking and Analyzing NLP
Adversarial examples highlight model vulnerabilities and are useful for evaluation and interpretation. We define universal adversarial triggers: input-agnostic sequences of tokens that trigger a…
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Model-Agnostic Interpretability of Machine Learning
TLDR
This paper argues for explaining machine learning predictions using model-agnostic approaches, treating the machine learning models as black-box functions, which provide crucial flexibility in the choice of models, explanations, and representations, improving debugging, comparison, and interfaces for a variety of users and models.
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Calibrate Before Use: Improving Few-Shot Performance of Language Models
TLDR
This work first estimates the model's bias towards each answer by asking for its prediction when given the training prompt and a content-free test input such as "N/A", and then fits calibration parameters that cause the prediction for this input to be uniform across answers.
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Semantically Equivalent Adversarial Rules for Debugging NLP models
TLDR
This work presents semantically equivalent adversaries (SEAs) – semantic-preserving perturbations that induce changes in the model’s predictions that induce adversaries on many instances that are extremely similar semantically.
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