title: Achieving Tokenizer Flexibility in Language Models through Heuristic
    Adaptation and Supertoken Learning
summary: Pretrained language models (LLMs) are often constrained by their fixed
  tokenization schemes, leading to inefficiencies and performance limitations,
  particularly for multilingual or specialized applications. This tokenizer
  lock-in presents significant challenges. standard methods to overcome this
  often require prohibitive computational resources. Although tokenizer
  replacement with heuristic initialization aims to reduce this burden, existing
  methods often require exhaustive residual fine-tuning and still may not fully
  preserve semantic nuances or adequately address the underlying compression
  inefficiencies. Our framework introduces two innovations: first, Tokenadapt, a
  model-agnostic tokenizer transplantation method, and second, novel
  pre-tokenization learning for multi-word Supertokens to enhance compression and
  reduce fragmentation. Tokenadapt initializes new unique token embeddings via a
  hybrid heuristic that combines two methods: a local estimate based on subword
  decomposition using the old tokenizer, and a global estimate utilizing the
  top-k semantically similar tokens from the original vocabulary. This
  methodology aims to preserve semantics while significantly minimizing
  retraining requirements. Empirical investigations validate both contributions:
  the transplantation heuristic successfully initializes unique tokens, markedly
  outperforming conventional baselines and sophisticated methods including
  Transtokenizer and ReTok, while our Supertokens achieve notable compression
  gains. Our zero-shot perplexity results demonstrate that the TokenAdapt hybrid
  initialization consistently yields lower perplexity ratios compared to both
  ReTok and TransTokenizer baselines across different base models and newly
  trained target tokenizers. TokenAdapt typically reduced the overall perplexity
  ratio significantly compared to ReTok, yielding at least a 2-fold improvement
  in these aggregate scores.
paper_authors._id: 6826c755f032d814754cadfe
paper_authors.name: Shaurya Sharthak
paper_authors._id: 6826c755f032d814754cadff
paper_authors.name: Vinayak Pahalwan
paper_authors._id: 6826c755f032d814754cae00
paper_authors.name: Adithya Kamath
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-16T07:11:49.739Z
paper_authors._id: 6523d85b27d1f3d84ab3a0a4
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/6523d85b27d1f3d84ab3a0a4/GF159gtSvxSWR9TAJIQby.jpeg
paper_authors.fullname: Adithya Kamath
paper_authors.type: user
paper_authors.user: adi-kmt
paper_authors._id: 6826c755f032d814754cae01
paper_authors.name: Adarsh Shirawalmath
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2025-05-16T05:19:45.785Z
paper_authors._id: 644bf6ef778ecbfb977e8e84
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/644bf6ef778ecbfb977e8e84/x2zFOkSIhDHf0LEZ1wzbU.jpeg
paper_authors.fullname: Adarsh AS
paper_authors.type: user
paper_authors.user: adarshxs
paper_title: Achieving Tokenizer Flexibility in Language Models through Heuristic
    Adaptation and Supertoken Learning
paper_summary: Pretrained language models (LLMs) are often constrained by their fixed
  tokenization schemes, leading to inefficiencies and performance limitations,
  particularly for multilingual or specialized applications. This tokenizer
  lock-in presents significant challenges. standard methods to overcome this
  often require prohibitive computational resources. Although tokenizer
  replacement with heuristic initialization aims to reduce this burden, existing
  methods often require exhaustive residual fine-tuning and still may not fully
  preserve semantic nuances or adequately address the underlying compression
  inefficiencies. Our framework introduces two innovations: first, Tokenadapt, a
  model-agnostic tokenizer transplantation method, and second, novel
  pre-tokenization learning for multi-word Supertokens to enhance compression and
  reduce fragmentation. Tokenadapt initializes new unique token embeddings via a
  hybrid heuristic that combines two methods: a local estimate based on subword
  decomposition using the old tokenizer, and a global estimate utilizing the
  top-k semantically similar tokens from the original vocabulary. This
  methodology aims to preserve semantics while significantly minimizing
  retraining requirements. Empirical investigations validate both contributions:
  the transplantation heuristic successfully initializes unique tokens, markedly
  outperforming conventional baselines and sophisticated methods including
  Transtokenizer and ReTok, while our Supertokens achieve notable compression
  gains. Our zero-shot perplexity results demonstrate that the TokenAdapt hybrid
  initialization consistently yields lower perplexity ratios compared to both
  ReTok and TransTokenizer baselines across different base models and newly
  trained target tokenizers. TokenAdapt typically reduced the overall perplexity
  ratio significantly compared to ReTok, yielding at least a 2-fold improvement
  in these aggregate scores.
paper_ai_summary: A new framework introduces Tokenadapt for tokenizer transplantation and Supertokens for improved pre-tokenization to enhance compression and reduce inefficiencies in pretrained language models.
paper_ai_keywords: pretrained language models Tokenadapt tokenizer transplantation Supertokens pre-tokenization learning subword decomposition zero-shot perplexity