Summary of The Foundations Of Tokenization: Statistical and Computational Concerns, by Juan Luis Gastaldi et al.
The Foundations of Tokenization: Statistical and Computational Concerns
by Juan Luis Gastaldi, John Terilla, Luca Malagutti, Brian DuSell, Tim Vieira, Ryan Cotterell
First submitted to arxiv on: 16 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A unified formal framework is proposed to represent and analyze tokenizer models in NLP, filling the theoretical gap on the impact of tokenization on statistical estimation. The framework, based on stochastic maps, establishes general conditions for principled use of tokenizers and necessary/sufficient conditions for consistency preservation. Additionally, it discusses crucial concerns like inconsistency, ambiguity, tractability, and boundedness for designing/ implementing tokenizer models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Tokenization is an important step in NLP that helps improve model performance but also causes issues like spurious ambiguity or inconsistency. The paper proposes a framework to represent and analyze tokenizer models, which can help build robust theoretical foundations for neural language modeling. This will inform future research and provide a better understanding of how tokenization affects statistical estimation. |
Keywords
» Artificial intelligence » Nlp » Tokenization » Tokenizer
