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cs.LG2025
Teaching LLMs How to Learn with Contextual Fine-Tuning
Younwoo Choi, Muhammad Adil Asif, Ziwen Han +2
Prompting Large Language Models (LLMs), or providing context on the expected model of operation, is an effective way to steer the outputs of such models to satisfy human desiderata…
cs.LG2024
Filtered not Mixed: Stochastic Filtering-Based Online Gating for Mixture of Large Language Models
Raeid Saqur, Anastasis Kratsios, Florian Krach +5
We propose MoE-F - a formalized mechanism for combining pre-trained Large Language Models (LLMs) for online time-series prediction by adaptively forecasting the best weighting…
cs.LG2023
FlexModel: A Framework for Interpretability of Distributed Large Language Models
Matthew Choi, Muhammad Adil Asif, John Willes +1
With the growth of large language models, now incorporating billions of parameters, the hardware prerequisites for their training and deployment have seen a corresponding increase.…