Can AI avoid generating duplicate sentences?

Modern AI systems can generally avoid generating identical duplicate sentences within a single output sequence. However, achieving this consistently depends heavily on the model's specific architecture, training, and the provided instructions.

Key mechanisms involve the AI's inherent use of probabilistic sampling during text generation, where it selects the next word based on predicted probabilities, inherently favouring novelty. Techniques like temperature setting and top-k/p sampling allow fine-tuning randomness. Crucial factors include sufficient context length for the model to recall recent output, explicit prompting against repetition, and the model's fundamental capability. While highly advanced models are adept at avoiding verbatim repetition in coherent responses, unintended repetition or paraphrasing can occur without careful configuration, especially if the prompt inadvertently suggests it or when generating extensive content.

Effective duplication avoidance has significant value in applications demanding diverse and creative outputs, such as content creation, story generation, summarization, and synthetic data production. Implementing appropriate settings and clear instructions minimizes redundancy, enhances the quality and originality of the AI's responses, and improves user experience in text-generation tasks.