Large Language Model Tokenizer Bias: A Case Study and Solution on GPT-4o
<|reference_start|>Large Language Model Tokenizer Bias: A Case Study and Solution on GPT-4o: Recent advancements in large language models (LLMs), such as GPT-4 and GPT-4o, have shown exceptional performance, especially in languages with abundant resources like English, thanks to extensive datasets that ensure robust training. Conversely, these models exhibit limitations when processing under-resourced languages such as Chinese and Korean, where issues including hallucinatory responses remain prevalent. This paper traces the roots of these disparities to the tokenization process inherent to these models. Specifically, it explores how the tokenizer vocabulary, often used to speed up the tokenization process and reduce tokens but constructed independently of the actual model training data, inadequately represents non-English languages. This misrepresentation results in the propagation of 'under-trained' or 'untrained' tokens, which perpetuate biases and pose serious concerns related to data security and ethical standards. We aim to dissect the tokenization mechanics of GPT-4o, illustrating how its simplified token-handling methods amplify these risks and offer strategic solutions to mitigate associated security and ethical issues. Through this study, we emphasize the critical need to rethink tokenization frameworks to foster more equitable and secure AI technologies.<|reference_end|>
@article{yang2024problematic,
    title={Problematic Tokens: Tokenizer Bias in Large Language Models},
    author={Jin Yang, Zhiqiang Wang, Yanbin Lin, Zunduo Zhao},
    journal={arXiv preprint arXiv:2406.11214},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.11214},
    primaryClass={cs.CL}
}