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Zero-shot LLM agents struggle to predict wellbeing scores from longitudinal data, often performing no better than a basic mean baseline.
WEQA achieves a 24% accuracy boost in wearable health question answering by dynamically adapting to the complexities of sensor data and user queries.
Achieve zero-shot sensor understanding across diverse sensor setups by repurposing a pretrained language model with a novel sensor-language alignment framework.
