This research investigates ways to help large language models interpret time-series sensor data by augmenting measurements with statistical summaries, detected patterns, and environmental context. The study evaluates baseline LLMs, fine-tuned models, and retrieval-augmented generation approaches, finding that combining specialized training with contextual information significantly improves grounding, actionability, and pattern recognition while reducing hallucinations.
* Augmenting time-series data with social and environmental context
* Comparing RAG frameworks against baseline and fine-tuned LLMs
* Enhancing the reliability of automated sensor monitoring systems
* Augmenting time-series data with social and environmental context
* Comparing RAG frameworks against baseline and fine-tuned LLMs
* Enhancing the reliability of automated sensor monitoring systems
