IoT-LM: Large Multisensory Language Models for the Internet of Things
Shentong Mo, Russ Salakhutdinov, Louis-Philippe Morency, Paul Pu Liang

TL;DR
This paper introduces IoT-LM, a large multisensory language model designed for IoT data, leveraging a vast dataset and a new adapter layer to enhance IoT understanding, reasoning, and interaction capabilities.
Contribution
The paper presents IoT-LM, the first multisensory language model for IoT, supported by MultiIoT dataset and a novel adapter layer for multisensory pre-training and instruction tuning.
Findings
Significant improvements on 8 IoT classification tasks
Enhanced interactive question-answering and reasoning with IoT data
Open-source release of data and modeling framework
Abstract
The Internet of Things (IoT) network integrating billions of smart physical devices embedded with sensors, software, and communication technologies is a critical and rapidly expanding component of our modern world. The IoT ecosystem provides a rich source of real-world modalities such as motion, thermal, geolocation, imaging, depth, sensors, and audio to recognize the states of humans and physical objects. Machine learning presents a rich opportunity to automatically process IoT data at scale, enabling efficient inference for understanding human wellbeing, controlling physical devices, and interconnecting smart cities. To realize this potential, we introduce IoT-LM, an open-source large multisensory language model tailored for the IoT ecosystem. IoT-LM is enabled by two technical contributions: the first is MultiIoT, the most expansive unified IoT dataset to date, encompassing over 1.15…
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Taxonomy
TopicsPublic Relations and Crisis Communication · Food Supply Chain Traceability
MethodsAdapter
