SenseCF: LLM-Prompted Counterfactuals for Intervention and Sensor Data Augmentation

TL;DR

This paper introduces SenseCF, a novel method using large language models to generate counterfactual explanations for sensor data, improving model robustness and interpretability in clinical prediction tasks.

Contribution

SenseCF leverages LLMs for zero-shot and few-shot counterfactual generation, outperforming traditional methods in plausibility, validity, and data augmentation effectiveness.

Findings

High plausibility and validity of generated CFs

Improved classifier accuracy with augmented data

Effective in low-data regimes for clinical datasets

Abstract

Counterfactual explanations (CFs) offer human-centric insights into machine learning predictions by highlighting minimal changes required to alter an outcome. Therefore, CFs can be used as (i) interventions for abnormality prevention and (ii) augmented data for training robust models. In this work, we explore large language models (LLMs), specifically GPT-4o-mini, for generating CFs in a zero-shot and three-shot setting. We evaluate our approach on two datasets: the AI-Readi flagship dataset for stress prediction and a public dataset for heart disease detection. Compared to traditional methods such as DiCE, CFNOW, and NICE, our few-shot LLM-based approach achieves high plausibility (up to 99%), strong validity (up to 0.99), and competitive sparsity. Moreover, using LLM-generated CFs as augmented samples improves downstream classifier performance (an average accuracy gain of 5%), especially in low-data regimes. This demonstrates the potential of prompt-based generative techniques to enhance explainability and robustness in clinical and physiological prediction tasks. Code base: github.com/shovito66/SenseCF.