When Does Reasoning Help in Machine Translation? A Hierarchical Analysis of LRM Reasoning Traces
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Computer Science > Computation and Language
Title:When Does Reasoning Help in Machine Translation? A Hierarchical Analysis of LRM Reasoning Traces
Abstract:Large Reasoning Models increasingly use intermediate traces for machine translation, but it remains unclear when such reasoning helps or hurts. We analyze reasoning traces across models, languages, domains, and datasets, focusing on reasoning language, length, and structure. We find that the best reasoning language is model-specific, reasoning length has a non-monotonic relationship with quality, and traces exhibit recurring functional patterns. To uncover these patterns, we introduce Hierarchical Meta-Summarization (HMS), a scalable framework that induces coarse- and fine-grained reasoning structures without predefined taxonomies. HMS reveals a shared organization--understanding/planning, translating/drafting, and refining/verifying--alongside domain-specific variation. Our results suggest that MT reasoning should be controlled in a model-aware, length-aware, and pattern-aware manner rather than uniformly encouraged.
| Comments: | Accepted to EMNLP 2026 Main |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2609.21247 [cs.CL] |
| (or arXiv:2609.21247v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2609.21247
arXiv-issued DOI via DataCite (pending registration)
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