Papers by Seungil Chad Lee

1 papers
Exploring In-context Example Generation for Machine Translation (2025.findings-acl)

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Challenge: Large language models (LLMs) have demonstrated strong performance across various tasks with just a few examples.
Approach: They propose a method that generates in-context example pairs without external resources.
Outcome: The proposed method builds upon two prior criteria, relevance and diversity, which have been highlighted as key factors for in-context example selection.

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