arXiv:2606.03259v1 Announce Type: new Abstract: Translation quality depends on purpose: the same source text demands different translations depending on audience, tone, and communicative intent. Yet MT models and metrics treat translation as a fixed mapping from source to target. LLMs enable users to explicitly specify purpose alongside source text, yet this capability has not been evaluated at scale. We introduce a systematic evaluation of purpose-driven MT across 50 languages, 5 model sizes and 8 text domains. We find that (1) explicit instructions substantially improve translation adaptedne
The proliferation of increasingly capable LLMs has opened avenues for more sophisticated, context-aware machine translation, making this evaluation timely.
This research provides a scalable validation of explicit instruction-based customization in MT, directly addressing a critical limitation in current translation systems.
Machine translation is evolving beyond fixed mappings to purpose-driven, adaptive outputs, significantly improving its utility and accuracy for specific use cases.
- · Businesses with global operations
- · Multilingual content creators
- · AI developers
- · End-users of translation tools
- · Generic machine translation providers
- · Translators relying solely on rote translation
- · Companies with undifferentiated MT offerings
Machine translation becomes significantly more usable and reliable for nuanced communication.
This leads to an increase in cross-lingual communication and content creation, possibly expanding global markets.
The enhanced capability of MT could accelerate the development of AI agents that operate effectively across language barriers, integrating into diverse global workflows.
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Read at arXiv cs.CL