Multilingual large language models (mLLMs) achieve strong performance in machine translation, yet our understanding of the mechanisms by which they transform representations from one language to another remains incomplete. Prior work suggests that translation decomposes into separable processes within an mLLM, where conceptual content is first represented independently, followed by a production into language-specific form.

In this work, we show that translation is even more modular than previously assumed and that the output language production in translation processes is actually further separable into a syntax and a surface language process. We construct controlled multilingual datasets that isolate cross-linguistic differences in word order and use causal interventions and probing to track how representations are transformed during translation. We find that models first construct target-side word order before realizing the target language surface form. We identify individual attention heads that are selectively sensitive to syntactic transformations while remaining largely invariant to language identity. These results establish the commitment to a syntactic structure as an independent stage in translation, extending prior decompositions and showing how translation is implemented by functionally different components within mLLMs.

Readers of English — but not Dutch or German — consistently show a grammaticality illusion: they find ungrammatical double-center-embedded sentences easier to process than corresponding grammatical sentences. If pre-trained language model (LM) surprisal mimics these cross-linguistic patterns, this implies that language statistics explain the effect; if, however, the illusion requires memory constraints such as lossy context surprisal (LCS), this suggests a critical role for memory. We evaluate LMs in Dutch, German, and English. We find that both factors influence LMs’ susceptibility to grammaticality illusions, and neither fully account for human-like processing patterns.