Summary of Exact Byte-level Probabilities From Tokenized Language Models For Fim-tasks and Model Ensembles, by Buu Phan et al.
Exact Byte-Level Probabilities from Tokenized Language Models for FIM-Tasks and Model Ensembles
by Buu Phan, Brandon Amos, Itai Gat, Marton Havasi, Matthew Muckley, Karen Ullrich
First submitted to arxiv on: 11 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers investigate the impact of tokenization on language models’ performance by analyzing the stochastic behavior of tokenized and byte-level models. They discover a phenomenon called “tokenization bias” where even statistically equivalent models can have different predictive distributions over the next byte. To address this issue, they introduce the Byte-Token Representation Lemma and develop a next-byte sampling algorithm that eliminates tokenization bias without requiring further training or optimization. This method enables zero-shot conversion of tokenized LMs into statistically equivalent token-free ones. The authors demonstrate its applicability in fill-in-the-middle tasks and model ensembles, achieving improved performance in coding benchmarks and reasoning tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Tokenization is a key component of language models that can impact their performance. Researchers have discovered a phenomenon called “tokenization bias” where even statistically equivalent models can have different predictive distributions over the next byte. To address this issue, they introduce the Byte-Token Representation Lemma and develop a method to eliminate tokenization bias without requiring further training or optimization. This method can be used in various applications such as fill-in-the-middle tasks and model ensembles. |
Keywords
» Artificial intelligence » Optimization » Token » Tokenization » Zero shot
