Rethinking LLM Ensembling from the Perspective of Mixture Models

arXiv:2605.00419v2 Announce Type: replace-cross Abstract: Model ensembling is a well-established technique for improving the performance of machine learning models. Conventionally, this involves averaging the output distributions of multiple models and selecting the most probable label. This idea has been naturally extended to large language models (LLMs), yielding improved performance but incurring substantial computational cost. This inefficiency stems from directly applying conventional ensemble implementation to LLMs, which require a separate forward pass for each model to explicitly compu

Source: arXiv cs.CL — read the full report at the original publisher.

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