Interpretation Quality Score for Measuring the Quality of interpretability methods

TL;DR

This paper introduces a new metric called Interpretation Quality Score to evaluate and compare the effectiveness of different interpretability methods in NLP models, addressing a gap in standardized evaluation.

Contribution

It proposes a novel, standardized metric for assessing the quality of explanations from interpretability methods in NLP, enabling better comparison and evaluation.

Findings

The metric was applied to three NLP tasks.

Six interpretability methods were evaluated.

Results demonstrate the metric's ability to differentiate explanation quality.

Abstract

Machine learning (ML) models have been applied to a wide range of natural language processing (NLP) tasks in recent years. In addition to making accurate decisions, the necessity of understanding how models make their decisions has become apparent in many applications. To that end, many interpretability methods that help explain the decision processes of ML models have been developed. Yet, there currently exists no widely-accepted metric to evaluate the quality of explanations generated by these methods. As a result, there currently is no standard way of measuring to what degree an interpretability method achieves an intended objective. Moreover, there is no accepted standard of performance by which we can compare and rank the current existing interpretability methods. In this paper, we propose a novel metric for quantifying the quality of explanations generated by interpretability methods. We compute the metric on three NLP tasks using six interpretability methods and present our results.