Time2Lang learns a lightweight adapter that maps time-series foundation model embeddings into a frozen LLM's input space, enabling mental health classification from wearable data without text prompting.
Jolt: jointly learned representations of language and time-series for clinical time-series interpretation (student abstract)
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Time2Lang: Bridging Time-Series Foundation Models and Large Language Models for Health Sensing Beyond Prompting
Time2Lang learns a lightweight adapter that maps time-series foundation model embeddings into a frozen LLM's input space, enabling mental health classification from wearable data without text prompting.