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This research dissects the internal mechanics of multilingual large language models, revealing that translation involves distinct stages where syntactic structure is constructed independently before surface language forms are applied. By isolating specific attention heads responsible for word-order transformations, the study provides critical insight into how mLLMs modularly separate conceptual content from linguistic syntax. These findings advance our understanding of the representational geometry within transformer architectures, offering a clearer map of the cognitive processes underlying cross-lingual generation.
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Separating Syntax from Language: A Mechanistic Account of Translation in Multilingual LLMs

arXiv2 min de cititArticolul și perspectiva sunt în engleză.

arXiv:2609.01356v1 Announce Type: new Abstract: 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.

Abstract: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.

Computation and Language (cs.CL)

arXiv:2609.01356 [cs.CL]

(or arXiv:2609.01356v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2609.01356 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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