Theoretical Analysis of Byte-Pair Encoding

• URL: http://arxiv.org/abs/2411.08671v1
• Date: Wed, 13 Nov 2024 15:04:02 GMT
• Title: Theoretical Analysis of Byte-Pair Encoding
• Authors: László Kozma, Johannes Voderholzer,
• Abstract summary: Byte-Pair (BPE) is a widely used method for subword tokenization. We show that BPE approximates the compression utility of the optimal pair encoding to a worst-case factor.
• Score: 0.8655526882770742
• License:
• Abstract: Byte-Pair Encoding (BPE) is a widely used method for subword tokenization, with origins in grammar-based text compression. It is employed in a variety of language processing tasks such as machine translation or large language model (LLM) pretraining, to create a token dictionary of a prescribed size. Most evaluations of BPE to date are empirical, and the reasons for its good practical performance are not well understood. In this paper we focus on the optimization problem underlying BPE: finding a pair encoding that achieves optimal compression utility. We show that this problem is APX-complete, indicating that it is unlikely to admit a polynomial-time approximation scheme. This answers, in a stronger form, a question recently raised by Zouhar et al. On the positive side, we show that BPE approximates the compression utility of the optimal pair encoding to a worst-case factor between $0.333$ and $0.625$. Our results aim to explain the ongoing success of BPE and are, to our knowledge, the first rigorous guarantees on its compression utility that hold for all inputs.

Related papers

• BPE Gets Picky: Efficient Vocabulary Refinement During Tokenizer Training [8.012203293561196]
  Picky BPE is a modified BPE algorithm that carries out vocabulary refinement during tokenizer training. Our method improves vocabulary efficiency, eliminates under-trained tokens, and does not compromise text compression.
  arXiv  Detail & Related papers  (2024-09-06T20:12:34Z)
• Batching BPE Tokenization Merges [55.2480439325792]
  BatchBPE is an open-source pure Python implementation of the Byte Pair algorithm. It is used to train a high quality tokenizer on a basic laptop.
  arXiv  Detail & Related papers  (2024-08-05T09:37:21Z)
• Scaffold-BPE: Enhancing Byte Pair Encoding for Large Language Models with Simple and Effective Scaffold Token Removal [58.29382184006158]
  We propose Scaffold-BPE, which incorporates a dynamic scaffold token removal mechanism by parameter-free, computation-light, and easy-to-implement modifications to the original BPE method. On extensive experiments across language modeling and even machine translation, Scaffold-BPE consistently outperforms the original BPE.
  arXiv  Detail & Related papers  (2024-04-27T07:12:07Z)
• Training LLMs over Neurally Compressed Text [55.11828645767342]
  This paper explores the idea of training large language models (LLMs) over highly compressed text. We propose Equal-Info Windows, a novel compression technique whereby text is segmented into blocks that each compress to the same bit length. We demonstrate effective learning over neurally compressed text that improves with scale, and outperforms byte-level baselines by a wide margin on perplexity and inference speed benchmarks.
  arXiv  Detail & Related papers  (2024-04-04T17:48:28Z)
• Unpacking Tokenization: Evaluating Text Compression and its Correlation with Model Performance [34.641079276516926]
  We argue for the theoretical importance of compression, that can be viewed as 0-gram language modeling. We demonstrate the empirical importance of compression for downstream success of pre-trained language models. We show that there is a correlation between tokenizers' compression and models' downstream performance.
  arXiv  Detail & Related papers  (2024-03-10T17:02:53Z)
• Tokenization Is More Than Compression [14.939912120571728]
  Existing tokenization approaches like Byte-Pair. (BPE) originate from the field of data compression. We introduce PathPiece, a new tokenizer that segments a document's text into the minimum number of tokens for a given vocabulary.
  arXiv  Detail & Related papers  (2024-02-28T14:52:15Z)
• Quick Dense Retrievers Consume KALE: Post Training Kullback Leibler Alignment of Embeddings for Asymmetrical dual encoders [89.29256833403169]
  We introduce Kullback Leibler Alignment of Embeddings (KALE), an efficient and accurate method for increasing the inference efficiency of dense retrieval methods. KALE extends traditional Knowledge Distillation after bi-encoder training, allowing for effective query encoder compression without full retraining or index generation. Using KALE and asymmetric training, we can generate models which exceed the performance of DistilBERT despite having 3x faster inference.
  arXiv  Detail & Related papers  (2023-03-31T15:44:13Z)
• LCP-dropout: Compression-based Multiple Subword Segmentation for Neural Machine Translation [5.505045114759599]
  We propose a simple and effective preprocessing method for subword segmentation based on a data compression algorithm. BPE/BPE-dropout is one of the fastest and most effective method compared to conventional approaches. We propose LCP-dropout for multiple subword segmentation that improves BPE/BPE-dropout, and show that it outperforms various baselines in learning from especially small training data.
  arXiv  Detail & Related papers  (2022-02-28T07:49:07Z)
• Dynamic Programming Encoding for Subword Segmentation in Neural Machine Translation [80.38621085548013]
  This paper introduces Dynamic Programming (DPE) a new segmentation algorithm for tokenizing sentences into subword units. A mixed character-subword transformer is proposed, which enables exact log marginal likelihood estimation and exact MAP inference to find target segmentations.
  arXiv  Detail & Related papers  (2020-05-03T05:00:50Z)
• Byte Pair Encoding is Suboptimal for Language Model Pretraining [49.30780227162387]
  We analyze differences between unigram LM tokenization and byte-pair encoding (BPE) We find that the unigram LM tokenization method matches or outperforms BPE across downstream tasks and two languages. We hope that developers of future pretrained LMs will consider adopting the unigram LM method over the more prevalent BPE.
  arXiv  Detail & Related papers  (2020-04-07T21:21:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.