A study titled "Uncertainty-Aware Hybrid Inference With On-Device Small and Remote Large Language Models" has been accepted for presentation at the 2025 IEEE International Conference on Machine Learning for Communication and Networking (ICMLCN).
Led by postdoctoral researcher Seungeun Oh and graduate student Jinhyuk Kim, the research introduces an innovative algorithm designed to enhance inference speed and efficiency in Hybrid Language Models (HLM).
The proposed algorithm dynamically measures uncertainty during Large Language Model (LLM) inference to optimize processing. When uncertainty is high, it seamlessly switches to a more powerful remote model for a comprehensive response. Conversely, for low-uncertainty cases, the on-device model delivers rapid responses, significantly reducing wait times.
This intelligent approach aims to boost efficiency, reduce computational costs, and improve user experience in AI-driven applications. The team's findings are expected to have a major impact on the future of real-time AI inference and edge computing.
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