Pathologies of Neural Models Make Interpretations Difficult (1804.07781v3)

Abstract: One way to interpret neural model predictions is to highlight the most important input features---for example, a heatmap visualization over the words in an input sentence. In existing interpretation methods for NLP, a word's importance is determined by either input perturbation---measuring the decrease in model confidence when that word is removed---or by the gradient with respect to that word. To understand the limitations of these methods, we use input reduction, which iteratively removes the least important word from the input. This exposes pathological behaviors of neural models: the remaining words appear nonsensical to humans and are not the ones determined as important by interpretation methods. As we confirm with human experiments, the reduced examples lack information to support the prediction of any label, but models still make the same predictions with high confidence. To explain these counterintuitive results, we draw connections to adversarial examples and confidence calibration: pathological behaviors reveal difficulties in interpreting neural models trained with maximum likelihood. To mitigate their deficiencies, we fine-tune the models by encouraging high entropy outputs on reduced examples. Fine-tuned models become more interpretable under input reduction without accuracy loss on regular examples.

• The paper demonstrates that neural models can maintain high-confidence predictions using irrelevant inputs even after key words are removed.
• It employs input reduction to systematically reveal overconfidence and spurious correlations across tasks like SQuAD, SNLI, and VQA.
• The study proposes entropy regularization to better align model uncertainty with human expectations, paving the way for more reliable AI systems.

Analyzing the Pathologies of Neural Models in Interpretation

The paper "Pathologies of Neural Models Make Interpretations Difficult" offers an in-depth examination of the challenges faced in interpreting neural network predictions, specifically within the domain of NLP. This research investigates the inherent pathologies of neural models that render current interpretation methods insufficient, and proposes novel strategies for mitigating these deficiencies.

Key Insight and Methodology

The cornerstone of interpretation methods for neural models is feature attribution, typically visualized as heatmaps that highlight important input features. However, the established methods, such as input perturbation and gradient-based importance measures, have limitations. They calculate the significance of a word by either the change in confidence when the word is removed or by analyzing the gradient with respect to that word. Yet, these methods often fail to account for unanticipated behaviors of neural models.

To expose these pathologies, the authors employ a technique called input reduction, which methodically removes the least important words while ensuring the model's predictions remain unchanged. This reduction process unveils that neural networks often rely on nonsensical inputs—words that appear irrelevant to humans. Consequently, these reduced inputs demonstrate that neural models can maintain consistent predictions with high confidence, even when these inputs would not logically support any prediction for human evaluators.

Experimental Results

The paper provides empirical evidence across three tasks: SQuAD for reading comprehension, SNLI for textual entailment, and VQA for visual question answering. By applying input reduction, the paper shows that models can frequently reduce input sentences to just one or two words without losing prediction accuracy—indicative of a model failure in proper interpretation and reliance on spurious correlations.

Human evaluations further confirm the contrived nature of such reduced inputs; participants struggle to achieve the correct answers using the reduced inputs compared to the original versions, underscoring the models' puzzling level of confidence on effectively meaningless inputs.

Underlying Causes of Observed Pathologies

The observed pathologies stem primarily from two characteristics of neural models: overconfidence and second-order sensitivity. Neural models are notorious for exhibiting high confidence in predictions across inputs, including those vastly outside the training distribution. This overconfidence translates into their inability to adjust confidence levels appropriately for input queries that appear as rubbish examples to humans. Meanwhile, the sensitivity of models to small input alterations exacerbates the issue of inconsistent interpretations, as minute changes can lead to substantial interpretations' variability without significantly affecting the model's output.

Modulating Model Pathologies

To address these pathologies, the paper introduces an entropy regularization technique aimed at increasing model uncertainty for reduced examples. This technique, when applied, encourages models to produce outputs with higher entropy (less confidence) for such inputs, thus aligning better with human interpretability expectations. Fine-tuning models using this approach enhances their interpretability without compromising accuracy, suggesting that training objectives that include uncertainty estimation can significantly improve model reliability in real-world applications.

Implications and Future Directions

This research highlights intrinsic deficiencies in prevailing neural model interpretations and proposes a potential pathway for mitigating these with targeted regularization strategies. Future work should focus on extending and refining these techniques across broader datasets and architectural paradigms. The insights gained can inform superior training and interpretability frameworks, emphasizing the need for robust, reliable, and transparent AI systems that align with human cognitive models. Furthermore, this indicates broader avenues for research in understanding and improving model certainty calibration as a cornerstone for trustable AI applications.