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صفحه اصلی
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دومین همایش بین المللی هوش مصنوعی
A Systematic Review of Tokenizer Adaption Techniques for Large Language Models
نویسندگان :
Niloofar Mortazavi
1
Mostafa Amiri
2
Hesham Faili
3
1- University of Tehran
2- University of Tehran
3- University of Tehran
کلمات کلیدی :
cross-lingual adaptation،tokenizer transfer،tokenizer replacement،large language models
چکیده :
This systematic review synthesizes key research contributions in adapting Large Language Models (LLMs) to diverse linguistic contexts, focusing specifically on novel methods for transferring or replacing tokenizers and initializing associated vocabulary embeddings. The field of Large Language Model (LLM) adaptation has rapidly evolved beyond simple vocabulary expansion towards sophisticated strategies that aim for high-fidelity transfer with minimal post-hoc fine-tuning. Recent methods primarily focus on how to initialize the new embedding layer—the lexical interface—to maximize preservation of the LLM’s knowledge encoded in the frozen transformer core.
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بیشتر
ثمین همایش، سامانه مدیریت کنفرانس ها و جشنواره ها - نگارش 44.5.0
