The probabilistic interpretation of Latte's latent variables impacts its performance compared to other efficient approximations by providing a principled framework for defining attention weights based on learned concepts rather than direct pairwise comparisons between tokens. This approach allows Latte Transformer to capture higher-level semantic relationships within sequences while maintaining linear scalability with sequence length. By incorporating probabilistic reasoning into how similarities are measured, Latte can achieve comparable performance to standard attention mechanisms while enabling efficient computation over longer contexts without sacrificing accuracy or expressiveness.