ME reinterprets LLM ensembling as token-level sampling from a mixture model, enabling single-model invocation per token with claimed mathematical equivalence to full ensembling and measured speedups of 1.78x-2.68x.
Exact byte-level probabilities from tokenized 9 Rethinking LLM Ensembling from the Perspective of Mixture Models language models for fim-tasks and model ensembles
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Rethinking LLM Ensembling from the Perspective of Mixture Models
ME reinterprets LLM ensembling as token-level sampling from a mixture model, enabling single-model invocation per token with claimed mathematical equivalence to full ensembling and measured speedups of 1.78x-2.68x.