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This paper examines how large language models perform machine translation when prompted with varying scopes, such as translating into multiple related languages or using example-based conditioning. It highlights the impact of prompt design and demonstration similarity on translation outcomes.
This paper examines how large language models perform machine translation when prompted with varying scopes, such as translating into multiple related languages or using example-based conditioning. It highlights the impact of prompt design and demonstration similarity on translation outcomes.
AI-assisted summary based on the listed source.
Large language models (LLMs) are increasingly used as general-purpose translation systems, but their behavior is usually evaluated under a single prompt shape: translate one source sentence into one target language. In practice, users may ask for one target language, for several related languages at once, or for...
Understanding how different prompt scopes and example selections affect LLM translation can improve their practical use as versatile translation tools. This insight helps optimize LLM prompts for more accurate and context-aware multilingual translations.
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VQV surfaced this signal because it is recent, relevant to Open Source LLMs, connected to arXiv.
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