From Where Words Come: Efficient Regularization of Code Tokenizers Through Source Attribution

TL;DR

This paper introduces Source-Attributed BPE (SA-BPE), a regularization technique for code tokenizers that reduces under-trained tokens by addressing data source imbalance, enhancing efficiency and safety of Large Language Models.

Contribution

It proposes a novel regularization method for BPE tokenization, improving code tokenizer training by mitigating overfitting and source imbalance issues.

Findings

SA-BPE reduces under-trained tokens significantly.

The method maintains inference speed and quality.

It enhances the safety and efficiency of LLMs.

Abstract

Efficiency and safety of Large Language Models (LLMs), among other factors, rely on the quality of tokenization. A good tokenizer not only improves inference speed and language understanding but also provides extra defense against jailbreak attacks and lowers the risk of hallucinations. In this work, we investigate the efficiency of code tokenization, in particular from the perspective of data source diversity. We demonstrate that code tokenizers are prone to producing unused, and thus under-trained, tokens due to the imbalance in repository and language diversity in the training data, as well as the dominance of source-specific, repetitive tokens that are often unusable in future inference. By modifying the BPE objective and introducing merge skipping, we implement different techniques under the name Source-Attributed BPE (SA-BPE) to regularize BPE training and minimize overfitting, thereby substantially reducing the number of under-trained tokens while maintaining the same inference procedure as with regular BPE. This provides an effective tool suitable for production use.