<ul><li>Duplicate entries in training datasets can lead to over-fitting and give an illusion of better performance during training.</li><li>Deduplication is key to unbiased model training and ensures that the model encounters a diverse range of examples.</li><li>Lexical deduplication targets exact or near-exact matches, while semantic deduplication goes deeper by finding texts that are similar in meaning.</li><li>By implementing both lexical and semantic deduplication techniques, the dataset's quality is enhanced, leading to more robust and generalizable language models.</li></ul>

Effective Data Deduplication for Training Robust Language Models

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