June 6, 2024, 4:42 a.m. | Raeid Saqur, Anastasis Kratsios, Florian Krach, Yannick Limmer, Jacob-Junqi Tian, John Willes, Blanka Horvath, Frank Rudzicz

cs.LG updates on arXiv.org arxiv.org

arXiv:2406.02969v1 Announce Type: new
Abstract: We propose MoE-F -- a formalised mechanism for combining $N$ pre-trained expert Large Language Models (LLMs) in online time-series prediction tasks by adaptively forecasting the best weighting of LLM predictions at every time step. Our mechanism leverages the conditional information in each expert's running performance to forecast the best combination of LLMs for predicting the time series in its next step. Diverging from static (learned) Mixture of Experts (MoE) methods, MoE-F employs time-adaptive stochastic filtering …

abstract arxiv cs.ai cs.cl cs.lg every expert filtering forecasting information language language models large language large language models llm llms mixed moe prediction predictions q-fin.cp q-fin.mf series stochastic tasks type

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