When to Ensemble: Identifying Token-Level Points for Stable and Fast LLM
  Ensembling
Authors: name: Heecheol Yun, name: Kwangmin Ki, name: Junghyun Lee, name: Eunho Yang
Organization: KAIST AI

Ensembling Large Language Models (LLMs) has gained attention as a promising
approach to surpass the performance of individual models by leveraging their
complementary strengths. In particular, aggregating models' next-token
probability distributions to select the next token has been shown to be
effective in various tasks. However, while successful for short-form answers,
its application to long-form generation remains underexplored. In this paper,
we show that using existing ensemble methods in long-form generation requires a
careful choice of ensembling positions, since the standard practice of
ensembling at every token often degrades performance. We identify two key
factors for determining these positions: tokenization mismatch across models
and consensus in their next-token probability distributions. Based on this, we
propose SAFE, (Stable And Fast LLM Ensembling), a framework that selectively
ensembles by jointly considering these factors. To further improve stability,
we introduce a probability sharpening strategy that consolidates probabilities
spread across multiple sub-word tokens representing the same word into a single
representative token. Our experiments on diverse benchmarks, including MATH500
and BBH, demonstrate that SAFE outperforms existing methods in both accuracy
and efficiency, with gains achieved even when ensembling fewer than 1% of
tokens.