Generalization of Ensembling Gains to Stronger Future Models

Determine the magnitude of the performance gain that post-generation ensembling provides when applied to stronger future large language model constituents.

Background

The study evaluates post-generation Uniform and Naive-Bayes fusion using a fixed set of LLMs and a single cybersecurity-requirements corpus. Although the authors argue that the relative advantage of ensembling should persist as current models are replaced by newer ones, the absolute size of that advantage may change as the individual constituents become more capable.

The unresolved issue is therefore whether stronger future models leave substantial room for ensemble improvement, and, if so, how large the resulting gain will be. This question concerns the external and temporal generality of the reported ensembling benefits rather than the internal validity of the present corpus-level comparisons.