Is Attention Interpretable? (1906.03731v1)

Abstract: Attention mechanisms have recently boosted performance on a range of NLP tasks. Because attention layers explicitly weight input components' representations, it is also often assumed that attention can be used to identify information that models found important (e.g., specific contextualized word tokens). We test whether that assumption holds by manipulating attention weights in already-trained text classification models and analyzing the resulting differences in their predictions. While we observe some ways in which higher attention weights correlate with greater impact on model predictions, we also find many ways in which this does not hold, i.e., where gradient-based rankings of attention weights better predict their effects than their magnitudes. We conclude that while attention noisily predicts input components' overall importance to a model, it is by no means a fail-safe indicator.

Interpretability of Attention Mechanisms in NLP Models

The paper "Is Attention Interpretable?" by Sofia Serrano and Noah A. Smith investigates the interpretability of attention mechanisms in NLP models. Attention mechanisms have gained prominence for their ability to enhance model performance across various tasks, including machine translation and LLMing. However, the assumption that attention weights inherently reveal the importance of specific input components is contested in this paper.

Key Findings

1. Attention as a Noisy Predictor:
   • The research challenges the validity of using attention weights as direct indicators of input importance. Although some alignment between higher attention weights and impactful input components is observed, the authors find numerous cases where attention weights do not reliably predict their influence on model predictions.
2. Comparison with Gradient-Based Methods:
   • The paper assesses attention weights against gradient-based rankings. The results indicate that gradients often serve as a more consistent predictor of importance than attention weights themselves.
3. Multi-Weight Evaluation:
   • By removing multiple weights in order of their perceived importance, the authors demonstrate that attention weights frequently fail to pinpoint the minimal set of factors underpinning a model’s decision. Alternative ranking methods based on gradients tend to find more concise sets of critical elements.

Implications

From a practical perspective, these findings urge caution when employing attention weights for interpretability tasks. The paper suggests that relying solely on attention without considering other interpretive methods could lead to misleading conclusions about model behavior.

Theoretically, this research prompts a reevaluation of how interpretability is defined and measured in NLP models. It highlights the need for developing more robust interpretive techniques that can integrate information beyond mere attention weight magnitudes.

Future Directions

Future work could explore diverse attention formulations, such as multi-headed or sparse attention, to determine if these exhibit different interpretability characteristics. Additionally, extending this analysis to more complex tasks, such as machine translation or LLMing with broader output spaces, could yield insights into the role of attention in varied contexts.

Conclusion

The paper contributes to the ongoing discourse on model interpretability by demonstrating the limitations of attention as an interpretability tool. It underscores the necessity for improved methodologies that can more accurately map model decisions to input features. As NLP models continue to grow in complexity, developing comprehensive interpretability frameworks will remain a crucial area of research.