There is no necessary correlation between language and the correct set of laws to reference. The language of the question (or the answer, if for some reason they are not the same) is an orthogonal issue to the intended scope. There is no reason US laws couldn't be the relevant to a question asked in German (and, conversely, no reason US laws couldn't be wrong for a question asked in English, even if it was specifically and distinguishably US English.)
For most questions it does this pretty well (e.g. asking for the legal age to drink). However once the answer becomes more complex it starts to halucinate very quickly. The fact that some of the hallucinations are just translated US laws makes me think that the knowledge transfer between languages is probably not helping in instances like this.
In recent LLMs, filtered internet text is at the low end of the quality spectrum. The higher end is curated scientific papers, synthetic and rephrased text, RLHF conversations, reasoning CoTs, etc. English/Chinese/Python/JavaScript dominate here.
The issue is that when there's a difference in training data quality between languages, LLMs likely associate that difference with the languages if not explicitly compensated for.
IMO it would be far more impactful to generate and publish high-quality data for minority languages for current model trainers, than to train new models that are simply enriched with a higher percentage of low-quality internet scrapings for the languages.