用大模型生成假想数据，提升医疗预测的可解释性和模型鲁棒性。
SenseCF: LLM-Prompted Counterfactuals for Intervention and Sensor Data Augmentation
- 用GPT-4o-mini零样本/三样本生成符合逻辑的反事实样本。
- 生成样本在可解释性上达99%，使分类准确率平均提升5%。
- 适合临床与生理信号预测中数据少、需可解释性的场景。
反事实解释（CFs）通过指出最小改动即可改变预测结果，为机器学习提供以人为本的洞察。因此，CFs可用于异常预防干预和增强模型训练数据。本文探索使用大语言模型（如GPT-4o-mini）在零样本和三样本设置下生成CFs。我们在两个数据集上评估：用于压力预测的AI-Readi旗舰数据集和公开的心脏病检测数据集。相比传统方法（DiCE、CFNOW、NICE），本方法在可解释性（最高达99%）、有效性（最高0.99）和稀疏性方面表现优异。将LLM生成的反事实样本作为增强数据，使下游分类器性能提升，平均准确率提高5%，尤其在低数据场景下效果显著。这表明基于提示的生成技术在临床与生理预测任务中具有提升可解释性与鲁棒性的潜力。代码已开源：github.com/shovito66/SenseCF。
原文摘要 · Abstract (English)
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.
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