Fine-tuned LLMs produce plausible counterfactuals for health interventions and recover 20% F1 via data augmentation in label-scarce sensor datasets.
Prompting large language models for counterfactual generation: An empirical study,
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Counterfactual Modeling with Fine-Tuned LLMs for Health Intervention Design and Sensor Data Augmentation
Fine-tuned LLMs produce plausible counterfactuals for health interventions and recover 20% F1 via data augmentation in label-scarce sensor datasets.