SMAB: MAB based word Sensitivity Estimation Framework and its Applications in Adversarial Text Generation

TL;DR

This paper introduces SMAB, a scalable multi-armed bandit framework for estimating word sensitivity in text classifiers, enabling applications like adversarial text generation and sensitivity analysis across multiple tasks and languages.

Contribution

The paper presents a novel, scalable sensitivity estimation method using multi-armed bandits, improving efficiency over previous exponential-time approaches and demonstrating diverse applications.

Findings

Sensitivity correlates with classifier accuracy.

Guided perturbation improves adversarial attack success rate by 15.58%.

Sensitivity-based rewards enhance paraphrase generation by 12%.

Abstract

To understand the complexity of sequence classification tasks, Hahn et al. (2021) proposed sensitivity as the number of disjoint subsets of the input sequence that can each be individually changed to change the output. Though effective, calculating sensitivity at scale using this framework is costly because of exponential time complexity. Therefore, we introduce a Sensitivity-based Multi-Armed Bandit framework (SMAB), which provides a scalable approach for calculating word-level local (sentence-level) and global (aggregated) sensitivities concerning an underlying text classifier for any dataset. We establish the effectiveness of our approach through various applications. We perform a case study on CHECKLIST generated sentiment analysis dataset where we show that our algorithm indeed captures intuitively high and low-sensitive words. Through experiments on multiple tasks and languages, we show that sensitivity can serve as a proxy for accuracy in the absence of gold data. Lastly, we show that guiding perturbation prompts using sensitivity values in adversarial example generation improves attack success rate by 15.58%, whereas using sensitivity as an additional reward in adversarial paraphrase generation gives a 12.00% improvement over SOTA approaches. Warning: Contains potentially offensive content.