Counterfactual Modeling with Fine-Tuned LLMs for Health Intervention Design and Sensor Data Augmentation
arXiv:2601.14590v3 Announce Type: replace Abstract: Counterfactual explanations (CFEs) provide human-centric interpretability by identifying the minimal, actionable changes required to alter a machine learning model's prediction. Therefore, CFs can be used as (i) interventions for abnormality prevention and (ii) augmented data for training robust models. We conduct a comprehensive evaluation of CF generation using large language models (LLMs), including GPT-4 (zero-shot and few-shot) and two open-source models-BioMistral-7B and LLaMA-3.1-8B, in both pretrained and fine-tuned configurations. Us
The rapid advancement and accessibility of large language models (LLMs) are enabling novel applications in domains like healthcare and data augmentation, making this research timely.
This research demonstrates a critical pathway for LLMs to generate actionable interventions for health and to improve the robustness of AI models through synthetic data, impacting both practical application and AI development paradigms.
Fine-tuned LLMs are becoming viable tools for generating human-centric counterfactual explanations, moving beyond theoretical applications into practical tools for intervention design and data augmentation.
- · Healthcare AI developers
- · Patients benefiting from personalized interventions
- · AI model developers
- · Open-source LLM communities
- · Traditional CFE methods
- · Diagnosis-only AI systems
Increased adoption of LLMs for generating explainable AI outputs and synthetic healthcare data.
Improved efficacy and interpretability of AI systems in sensitive domains like medicine, leading to greater trust and broader deployment.
The development of 'AI agents' specialized in proactive intervention design and personalized health management at scale.
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