This article discusses an analytical framework for tokenization in AI models, detailing methods for detecting under-trained tokens using prompts tailored for base models. Key highlights include the effectiveness of specific indicators and observations based on model performance. Additionally, it outlines UTF-8 encoding principles, emphasizing its role in tokenization as it efficiently accommodates a diverse array of Unicode characters. The verification process is implemented through repeated prompts to ensure accuracy and reduce false positives during token detection.
We use three repetitive prompts to induce models to output the candidate token we are testing, ensuring no specialized tuning is required.
UTF-8 is the most prevalent encoding scheme used to represent text, efficiently encoding Unicode characters from various writing systems.
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