Papers by Grace Y Chung

1 papers
Tokenizer-Aware Cross-Lingual Adaptation of Decoder-Only LLMs through Embedding Relearning and Swapping (2026.eacl-long)

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Challenge: Large Language Models (LLMs) have been primarily focused on English, leaving the multilingual ability unexplored.
Approach: They propose a technique that creates new tokenizers and tunes embeddings on fixed model weights for target language adaptation.
Outcome: The proposed method is light-weight and performant but has limitations for older models and high resource languages.

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