<ul><li>The study focuses on detecting under-trained tokens in large language models through various indicators and verification techniques.</li><li>The effectiveness of the indicators was highlighted, showing a high predictive nature in detecting under-trained tokens.</li><li>Verification statistics and example verified tokens for different model families and tokenizer vocabulary sizes were presented in Table 1.</li><li>Authors of the study are Sander Land and Max Bartolo from Cohere, and the paper is available on arxiv under CC BY-SA 4.0 DEED license.</li></ul>

How Many Glitch Tokens Hide in Popular LLMs? Revelations from Large-Scale Testing

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