arXiv:2506.15138v2 Announce Type: replace Abstract: Tokenization directly affects the inference efficiency of large language models, since fragmented tokenization increases sequence length and generation cost. Although longer, multi-word tokens can reduce fertility, naively adding them often degrades language model performance. We propose Thunder-Tok, a subword tokenizer that reduces fertility while preserving downstream performance. Thunder-Tok first constructs a large seed vocabulary from corpus substrings and filters structurally incomplete candidates, including invalid Unicode byte fragmen
The continuous drive for efficiency in large language models necessitates novel approaches to fundamental components like tokenization, as current methods introduce significant overhead.
Improving tokenization directly impacts the cost and performance of large language models, making advanced AI applications more economically viable and performant.
New tokenization methods like Thunder-Tok promise to reduce computational costs for large language models without sacrificing performance, potentially accelerating AI development and deployment.
- · AI developers
- · Cloud providers
- · Large language model users
- · AI-driven SaaS companies
- · Inefficient AI models
- · Compute-intensive LLM architectures
Reduced inference costs for large language models will enable broader adoption and deployment of AI.
More efficient AI inference could lead to new classes of always-on or embedded AI applications currently constrained by performance or cost.
Lower compute barriers might democratize access to advanced AI capabilities, fostering innovation across smaller players and non-traditional sectors.
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