  Related works used indexes like CKA and variants of CCA to measure the
similarity of cross-lingual representations in multilingual language models. In
this paper, we argue that assumptions of CKA/CCA align poorly with one of the
motivating goals of cross-lingual learning analysis, i.e., explaining zero-shot
cross-lingual transfer. We highlight what valuable aspects of cross-lingual
similarity these indexes fail to capture and provide a motivating case study
\textit{demonstrating the problem empirically}. Then, we introduce
\textit{Average Neuron-Wise Correlation (ANC)} as a straightforward alternative
that is exempt from the difficulties of CKA/CCA and is good specifically in a
cross-lingual context. Finally, we use ANC to construct evidence that the
previously introduced ``first align, then predict'' pattern takes place not
only in masked language models (MLMs) but also in multilingual models with
\textit{causal language modeling} objectives (CLMs). Moreover, we show that the
pattern extends to the \textit{scaled versions} of the MLMs and CLMs (up to 85x
original mBERT).\footnote{Our code is publicly available at
\url{https://github.com/TartuNLP/xsim}}
