Can an Old Dog Be Taught New Tricks? Taking LLMs Beyond Sentence Level Translation
JA title: 大規模言語モデルは文単位を超えた翻訳が可能か：文書全体の文脈を活用した翻訳手法
What to read for
3 checks before implementation
- 01Article focus
翻訳ツールは通常、文ごとに翻訳しています。この研究は、AI が文書全体の意味をつかんで翻訳できるか、また過去の翻訳例を参考にすることで翻訳の質が上がるかを調べています。
- 02Operational lens
国際取引や多言語カスタマーサポートが必要な中堅企業で有用。
- 03Verify in source
arXiv (cs.CL) (Research): check the dataset, evaluation setup, and adoption assumptions.
Automatic translation systems, from CAT tools to MT, overwhelmingly treat translation as a sentence-by-sentence act. This paper asks whether LLMs can be moved beyond that paradigm through whole-document, corpus-informed translation. We present PAT (Pragmatic Auto-Translator), a RAG-based system that pairs user-configured specifications with context from a comparable corpus of authentic longform texts in U.S. English and Latin American Spanish, passing retrieved paragraph-, s…
ゼロビズAX View — How Japanese companies can apply it
国際取引や多言語カスタマーサポートが必要な中堅企業で有用。既存 CAT ツール（Trados など）の補完として、文書全体の文脈を保つ翻訳精度向上が期待できます。導入は研究段階のため市販ツール化待ち。参考となる翻訳コーパス構築がコスト要因。企業内用語辞書と組み合わせれば効果が高まる可能性があります。
Plain-language glossary
Simple explanations of the technical terms used in this article.
- RAG（社内データ検索つきAI）
- A setup where the AI first looks up relevant documents, then answers — so replies are grounded in real sources.
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Primary source: https://arxiv.org/abs/2607.14040v1
This article is an AI-generated summary of overseas primary sources. Translations or summaries may be imperfect — always confirm the original before making implementation decisions. The "ゼロビズAX View" is our applied interpretation and does not guarantee any specific outcome.
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