Accelerating the Global Aggregation of Local Explanations
Authors: Alon Mor, Yonatan Belinkov, Benny Kimelfeld
What
This paper tackles the challenge of efficiently identifying the top-k most impactful words in a document collection for explaining text classifiers, focusing on global aggregation of the Anchor algorithm’s local explanations.
Why
Global aggregation of local explanations like Anchor is computationally expensive, hindering online analysis. This work provides both a novel probabilistic aggregation method that improves the quality of results and runtime optimizations making it practical for interactive use.
How
The authors propose a probabilistic model (GPR) to estimate the importance of words as explanations, considering frequency and noise. They introduce runtime optimizations including incremental evaluation, candidate filtering, and adjusted hyperparameters for Anchor. Experiments evaluate the quality and speed of their approach across various datasets and classification tasks.
Result
GPR consistently outperforms baseline aggregations in identifying impactful terms. Optimizations, particularly increasing the confidence parameter (delta) in Anchor, significantly accelerate computation (up to 30x) with minimal or even positive impact on quality. Case studies demonstrate the interpretability of identified terms.
LF
Future work includes extending the approach to multi-word terms, adapting the optimizations to other local attribution methods, and exploring alternative document traversal orders during aggregation.
Abstract
Local explanation methods highlight the input tokens that have a considerable impact on the outcome of classifying the document at hand. For example, the Anchor algorithm applies a statistical analysis of the sensitivity of the classifier to changes in the token. Aggregating local explanations over a dataset provides a global explanation of the model. Such aggregation aims to detect words with the most impact, giving valuable insights about the model, like what it has learned in training and which adversarial examples expose its weaknesses. However, standard aggregation methods bear a high computational cost: a na”ive implementation applies a costly algorithm to each token of each document, and hence, it is infeasible for a simple user running in the scope of a short analysis session. % We devise techniques for accelerating the global aggregation of the Anchor algorithm. Specifically, our goal is to compute a set of top- words with the highest global impact according to different aggregation functions. Some of our techniques are lossless and some are lossy. We show that for a very mild loss of quality, we are able to accelerate the computation by up to 30, reducing the computation from hours to minutes. We also devise and study a probabilistic model that accounts for noise in the Anchor algorithm and diminishes the bias toward words that are frequent yet low in impact.