arXiv — NLP / Computation & Language · · 3 min read

Copy First, Translate Later: Interpreting Translation Dynamics in Multilingual Pretraining

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Computer Science > Computation and Language

arXiv:2604.17633 (cs)
[Submitted on 19 Apr 2026 (v1), last revised 26 Jun 2026 (this version, v2)]

Title:Copy First, Translate Later: Interpreting Translation Dynamics in Multilingual Pretraining

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Abstract:Large language models exhibit impressive cross-lingual capabilities. However, prior work analyzes this phenomenon through isolated factors and at sparse points during training, limiting our understanding of how cross-lingual generalization emerges--particularly in the early phases of learning. To study the early trajectory of linguistic and translation capabilities, we pretrain a multilingual 1.7B model on nine diverse languages, capturing checkpoints at a much finer granularity. We use word-level translation as a testbed, introducing a novel dataset to trace how translation develops over training through behavioral analyses, model-component analysis, and parameter-based ablations. We find that the model quickly acquires basic linguistic capabilities in parallel with token-level copying, while translation develops in two distinct phases: an initial phase dominated by copying and surface-level similarities, and a second phase in which more generalizing translation mechanisms are developed while copying is refined. Together, these findings provide a fine-grained view of how cross-lingual generalization develops during multilingual pretraining.
Comments: 10 pages
Subjects: Computation and Language (cs.CL)
Cite as: arXiv:2604.17633 [cs.CL]
  (or arXiv:2604.17633v2 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2604.17633
arXiv-issued DOI via DataCite

Submission history

From: Maria Matveev [view email]
[v1] Sun, 19 Apr 2026 22:03:29 UTC (2,752 KB)
[v2] Fri, 26 Jun 2026 09:55:26 UTC (3,231 KB)
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