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Exploring Prompt Engineering with GPT Language Models for Document-Level Machine Translation: Insights and Findings

Yangjian Wu, Gang Hu

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Abstract

This paper describes Lan-Bridge Translation systems for the WMT 2023 General Translation shared task. We participate in 2 directions: English to and from Chinese. With the emergence of large-scale models, various industries have undergone significant transformations, particularly in the realm of document-level machine translation. This has introduced a novel research paradigm that we have embraced in our participation in the WMT23 competition. Focusing on advancements in models such as GPT-3.5 and GPT-4, we have undertaken numerous prompt-based experiments. Our objective is to achieve optimal human evaluation results for document-level machine translation, resulting in our submission of the final outcomes in the general track.

Topics & Concepts

RealmMachine translationComputer scienceTranslation (biology)Task (project management)Natural language processingArtificial intelligenceBridge (graph theory)Software engineeringEngineeringSystems engineeringMedicineChemistryMessenger RNAInternal medicinePolitical scienceLawBiochemistryGeneNatural Language Processing TechniquesTopic ModelingMachine Learning and Data Classification
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