<ul data-eligibleForWebStory="true"><li>Introducing the Semantic-Information Uncertainty Principle (SIUP) for generative AI challenges coherence and accuracy.</li><li>Formalizes a trade-off between semantic coherence and factual fidelity in language models.</li><li>Proposes SIUP-regularized attention mechanism for optimal balance in AI-generated text.</li><li>Empirical validation is required for the theoretical framework presented in the article.</li></ul>

# Fine-Tuning Transformers with the Semantic-Information Uncertainty Principle (SIUP): A…

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