Summary of Data Mixture Inference: What Do Bpe Tokenizers Reveal About Their Training Data?, by Jonathan Hayase et al.
Data Mixture Inference: What do BPE Tokenizers Reveal about their Training Data?
by Jonathan Hayase, Alisa Liu, Yejin Choi, Sewoong Oh, Noah A. Smith
First submitted to arxiv on: 23 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 introduces a novel attack to uncover the distributional make-up of training data for language models. It leverages byte-pair encoding (BPE) tokenizers, which reveal information about token frequencies in their training data through ordered lists of merge rules. The authors formulate a linear program to solve for proportion of each category in the tokenizer’s training set and demonstrate its effectiveness on controlled experiments with known mixtures of natural languages, programming languages, and data sources. They apply this approach to off-the-shelf tokenizers released with recent LMs, confirming publicly disclosed information and making new inferences about their multilingual capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about understanding the composition of language models’ training data. It’s like trying to figure out what kind of music a musician likes based on the notes they play. The authors create a clever method using something called byte-pair encoding, which helps them understand where the words come from in these massive language models. They test their approach and find that it works well, even when looking at real-world examples. This information can help us better understand how these language models were trained and what they’re good for. |
Keywords
* Artificial intelligence * Token * Tokenizer
