Summary of Improving Self Consistency in Llms Through Probabilistic Tokenization, by Ashutosh Sathe et al.
Improving Self Consistency in LLMs through Probabilistic Tokenization
by Ashutosh Sathe, Divyanshu Aggarwal, Sunayana Sitaram
First submitted to arxiv on: 4 Jul 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 The paper explores the application of probabilistic tokenizations in large language models (LLMs), aiming to improve their performance. Despite previous research demonstrating the effectiveness of this approach, modern LLMs have not yet utilized probabilistic tokenizations during training. The study highlights that contemporary LLM tokenizers can generate multiple tokenizations, but this capability remains underutilized. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about using a special way to prepare words for language models. This method, called probabilistic tokenizations, helps the models learn better. Even though it has been proven to work well in past research, modern super-powerful language models haven’t used it yet. The important point is that these powerful models can do multiple versions of word preparation, but they’re not using this ability. |
