Text Counterfactuals via Latent Optimization and Shapley-Guided Search

TL;DR

This paper introduces a novel method for generating counterfactual texts by optimizing in latent space and using Shapley values to guide modifications, improving interpretability and debugging of classifiers.

Contribution

It proposes a new approach combining latent optimization and Shapley-guided search for counterfactual text generation, addressing challenges of discrete text modifications.

Findings

Outperforms recent baselines in success rate and quality

Latent optimization improves counterfactual relevance

Shapley values enhance the effectiveness of modifications

Abstract

We study the problem of generating counterfactual text for a classifier as a means for understanding and debugging classification. Given a textual input and a classification model, we aim to minimally alter the text to change the model's prediction. White-box approaches have been successfully applied to similar problems in vision where one can directly optimize the continuous input. Optimization-based approaches become difficult in the language domain due to the discrete nature of text. We bypass this issue by directly optimizing in the latent space and leveraging a language model to generate candidate modifications from optimized latent representations. We additionally use Shapley values to estimate the combinatoric effect of multiple changes. We then use these estimates to guide a beam search for the final counterfactual text. We achieve favorable performance compared to recent white-box and black-box baselines using human and automatic evaluations. Ablation studies show that both latent optimization and the use of Shapley values improve success rate and the quality of the generated counterfactuals.