arXiv:2605.22821v1 Announce Type: cross Abstract: Tokenisation is an integral part of the current NLP pipeline. Current tokenisation algorithms such as BPE and Unigram are greedy algorithms -- they make locally optimal decisions without considering the resulting vocabulary as a whole. We instead formulate tokeniser construction as a linear program and solve it using convex optimisation tools, yielding a new algorithm we call ConvexTok. We find ConvexTok consistently improves intrinsic tokenisation metrics and the bits-per-byte (BpB) achieved by language models; it also improves downstream task
The continuous improvement in AI models necessitates corresponding advances in fundamental components like tokenization, which is an active area of research to push performance boundaries.
Improved tokenization directly enhances the efficiency and performance of large language models, leading to better AI capabilities and resource utilization across various applications.
This new algorithm, ConvexTok, offers a more globally optimal approach to tokenization compared to existing greedy methods, potentially setting a new standard for NLP model pre-processing.
- · AI developers and researchers
- · NLP applications
- · Cloud AI providers
- · Hardware manufacturers for AI
- · Existing greedy tokenization algorithms
Language models become more efficient and achieve better performance on various NLP tasks.
Reduced computational demand or improved accuracy allows for more complex or larger AI models, expanding the scope of AI applications.
More sophisticated and nuanced AI interactions become possible, accelerating the development of advanced AI agents and automation across industries.
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Read at arXiv cs.LG