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A recent study reveals that large language models often adopt false information as truth during the fine-tuning process, even when that data is explicitly labeled as incorrect. Researchers discovered a phenomenon called "negation neglect," where models prioritize statistical patterns over warnings that certain claims are fictional or deceptive. This internal bias causes AI to hallucinate or justify fabrications because it struggles to process negative qualifiers attached to broad documents. The study found that even repeated warnings or attributing lies to unreliable sources failed to prevent the models from internalizing the misinformation. Interestingly, this issue primarily affects training data rather than real-time chat interactions, suggesting that how information is structured during learning is critical. To combat this, developers may need to use local negations that place denials within the same sentence as the false claim to ensure the AI recognizes the truth.