LBPE: Long-token-first Tokenization to Improve Large Language Models

• URL: http://arxiv.org/abs/2411.05504v1
• Date: Fri, 08 Nov 2024 12:03:36 GMT
• Title: LBPE: Long-token-first Tokenization to Improve Large Language Models
• Authors: Haoran Lian, Yizhe Xiong, Zijia Lin, Jianwei Niu, Shasha Mo, Hui Chen, Peng Liu, Guiguang Ding,
• Abstract summary: Long tokens, rich in semantic information, have fewer occurrences in tokenized datasets compared to short tokens. We propose LBPE, which prioritizes long tokens during the encoding process. Experiments across diverse language modeling tasks demonstrate that LBPE consistently outperforms the original BPE.
• Score: 26.3619552256488
• License:
• Abstract: The prevalent use of Byte Pair Encoding (BPE) in Large Language Models (LLMs) facilitates robust handling of subword units and avoids issues of out-of-vocabulary words. Despite its success, a critical challenge persists: long tokens, rich in semantic information, have fewer occurrences in tokenized datasets compared to short tokens, which can result in imbalanced learning issue across different tokens. To address that, we propose LBPE, which prioritizes long tokens during the encoding process. LBPE generates tokens according to their reverse ranks of token length rather than their ranks in the vocabulary, granting longer tokens higher priority during the encoding process. Consequently, LBPE smooths the frequency differences between short and long tokens, and thus mitigates the learning imbalance. Extensive experiments across diverse language modeling tasks demonstrate that LBPE consistently outperforms the original BPE, well demonstrating its effectiveness.

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